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April 4, 2011

Using Ecological Momentary Assessment to Determine Media Use by Individuals With and Without Major Depressive Disorder

Author Affiliations

Author Affiliations: Division of General Internal Medicine, Department of Medicine (Drs Primack and Switzer and Mr DeLozier), and Departments of Pediatrics (Dr Primack) and Psychiatry (Dr Silk), University of Pittsburgh School of Medicine, RAND Corporation (Dr Shadel), and Center for Heath Equity Research and Promotion, Veterans Affairs Pittsburgh Healthcare System (Dr Switzer), Pittsburgh, Pennsylvania; School of Journalism and Mass Communication, University of North Carolina, Chapel Hill (Dr Dillman Carpentier); and Community Health and Human Development and Joint Medical Program, School of Public Health, University of California, Berkeley (Dr Dahl).

Arch Pediatr Adolesc Med. 2011;165(4):360-365. doi:10.1001/archpediatrics.2011.27

Objective  To use ecological momentary assessment techniques to measure the association of major depressive disorder (MDD) with media use.

Design  Data were collected using an ecological momentary assessment protocol with cellular telephone–based brief interviews.

Setting  Participants received as many as 60 telephone calls from a trained staff member during 5 extended weekends in an 8-week period.

Participants  One hundred six adolescent participants who were part of a larger neurobehavioral study of depression in Pittsburgh from January 1, 2003, through December 31, 2008.

Main Exposure  At each call, participants were asked whether they were using the following 5 types of media: television or movies, music, video games, Internet, and print media, such as magazines, newspapers, and books.

Main Outcome Measures  We developed multivariable models to determine the independent association of each type of media use with MDD, controlling for sociodemographic variables.

Results  Of the 106 participants, 46 were diagnosed as having MDD. In multivariable models controlling for age, sex, and race, each increasing quartile of audio use was associated with an 80% increase in the odds of having MDD (odds ratio, 1.8; 95% confidence interval, 1.1-2.8; P = .01 for trend). Conversely, each increasing quartile of print media use was associated with a 50% decrease in the odds of having MDD (odds ratio, 0.5; 95% confidence interval, 0.3-0.9; P = .009 for trend).

Conclusions  Major depressive disorder is positively associated with popular music exposure and negatively associated with reading print media such as books. Further research elucidating the directionality and strength of these relationships may help advance understanding of the relationships between media use and MDD.

Major depressive disorder (MDD) is the leading cause of disability in the world.1 Because its onset is common in adolescence and young adulthood,2,3 it coincides with a pivotal period of physical and psychological development and can lead to substantial morbidity and mortality.4,5 The development of MDD is a biopsychosocial, multifactorial process influenced by multiple risk and protective factors.2,3,5-7 Moreover, given the high rates of media use in adolescence8,9 and the potential bidirectional effects between mood and media use,10-17 examining real-life media use in samples of depressed adolescents is an important area of research in adolescent affect regulation.

Current total media exposure for youth aged 8 to 18 years is estimated at more than 10 hours per day.8Cultural messages transmitted through media affect other behaviors related to mental health, such as eating disorders and aggressive behavior.14,15,17,18 Media use may similarly contribute to the development of depression via reinforcement of cognitions and/or affective states.15,17,19For example, certain televised media situations are saturated with highly idealized characters and situations,17,20 and constant comparison of one's self to these unattainable images may contribute to or exacerbate depression.19,21 Other media exposures—particularly on television, in music, and in video games—are highly negative or violent,16,17,22 and repeated exposure to these messages may engender a negative and fearful perception of the world, which can also contribute to or exacerbate depression.21

Being depressed may, in turn, influence media use. For example, individuals with MDD may lack the initiative and/or concentration to use media requiring high cognitive involvement, such as reading, but may be more likely to consume media with low cognitive involvement, such as television23-25 and radio.23,24 Thus, we might expect there to be a positive association between television or music exposure and MDD and a negative association between nonelectronic media exposure (such as reading books) and MDD. Similarly, individuals with MDD may seek out media with positive emotional content to find refuge from their negative emotions,26 or they may seek out negative emotional content to feel a sense of connection with others who are in pain27,28 or to cope with their own struggles.26,29

Determining which types of media use are most often associated with depression may assist with elucidation of these mechanisms linking media exposure and MDD, and the knowledge that MDD is associated with particular media exposures could help us effectively target screening, preventive, and/or therapeutic programs. To this end, we previously used the National Longitudinal Survey of Adolescent Health to investigate electronic media exposure in nondepressed middle adolescents and subsequent development of MDD after 7 years of follow-up. Those reporting higher television use had significantly greater odds of developing MDD (odds ratio [OR], 1.09; 95% confidence interval [CI], 1.02-1.16) for each additional hour of daily television use, even after controlling for multiple covariates.10 Also, MDD was inversely associated with exposure to books. However, there were no statistically significant associations between MDD and exposure to music, video games, or videocassettes.

Although these results were interesting, they were not necessarily consistent with other notable studies investigating associations between depression and media use.11,30 In addition, large survey measurements, such as those of the National Longitudinal Survey of Adolescent Health, can be imprecise and subject to recall bias. Ecological momentary assessment (EMA) is an ecologically valid method of gathering real-time data on context, behavior, and mood in natural environments through the use of signaling devices to minimize recall bias. Ecological momentary assessment uses various methods (eg, telephone calls and handheld computers) to monitor psychological phenomena in real time and real life.31-35 It combines the measurement precision of a laboratory setting with the heightened external validity of naturalistic quasi-experimental field studies.35 Thus, it has the potential to address the methodological gaps in this domain of inquiry and to strengthen the evidence of associations between mass media exposure and psychological dysfunction in an at-risk population.35,36

The purpose of this study was to use EMA measurement techniques to assess the association of MDD with various media exposures. Based on results of prior empirical and theoretical work described in the preceding paragraphs, we hypothesized that MDD would be positively associated most strongly with television but also moderately associated with music exposure. We also expected that MDD would be inversely associated with exposure to print media. Because of a lack of prior consistent data,10,11,13,30 we did not hypothesize that MDD would be significantly associated with other media exposures (ie, video games and Internet).


Context and participants

The data used in this study were collected as part of the Child and Adolescent Depression and Anxiety Study at the Western Psychiatric Institute and Clinic in Pittsburgh from January 1, 2003, through December 31, 2008. Participants were recruited from the following 3 sources: (1) community advertisements (primarily radio and newspaper), (2) inpatient and outpatient clinics at a major medical center in which the youth or their parents were being treated, and (3) referrals from other research studies or other participants in the present study. Participants were in a current episode of MDD (n = 46) or were healthy control participants with no history of psychiatric disorder (n = 60). Consistent with epidemiological reports of comorbidity rates,37 34 of the 46 participants with MDD (74%) had a comorbid anxiety disorder (separation anxiety disorder, generalized anxiety disorder, and/or social phobia).

Youth with MDD were required to meet diagnostic criteria according to Diagnostic and Statistical Manual of Mental Disorders (DSM) (Third Edition Revised)38 or DSM (Fourth Edition)39 classification for MDD. Healthy controls were required to be free of any lifetime psychopathology. In addition to media-related data, this study was designed to assess a number of biomedical factors established as relevant in our previous work.40,41 For this reason, we applied the following exclusionary criteria: (1) the use of any medication with central nervous system effects within the past 2 weeks; (2) significant medical illness; (3) extreme obesity (weight greater than 150% of ideal body weight) or growth failure (height or weight below the third percentile); (4) IQ of 70 or less; (5) inordinate fear of intravenous needles (because of the need to draw blood for biological assays); and (6) specific learning disabilities. Participants with MDD were also excluded if they had schizophrenic, schizoaffective, or bipolar disorders. After complete description of the study to the subjects and parents/guardians, written informed consent was obtained.

Initial psychiatric interview

Each youth and his or her parents were interviewed to determine the youth's psychiatric history, using the Schedule for Affective Disorders and Schizophrenia for School-Age Children–Present and Lifetime Version.42 Parents and youth were interviewed separately, with clinical interviewers integrating data from both informants to arrive at a final diagnosis. All interviews were conducted by trained BA- and MA-level research clinicians. Interrater reliabilities for diagnoses assessed during the course of this study were κ ≥ 0.70. The results of the interview were presented at a consensus case conference with a child psychiatrist, who reviewed the findings and preliminary diagnosis and provided a final diagnosis based on DSM-III-R or DSM-IV criteria.38,39

Data collection procedures

The study was approved by the University of Pittsburgh's institutional review board. Data were collected using a cellular telephone–based EMA protocol.36 Participants were given a modified answer-only cellular telephone on which they received telephone calls from a trained staff member during 5 extended weekends (Friday through Monday) in an 8-week period. Participants received 12 calls during each extended weekend (2 calls each on Friday and Monday from 4 to 10 PM and 4 calls each on Saturday and Sunday from 11 AM to 10 PM). Participants who did not initially answer their cellular telephone received a follow-up call 10 minutes later. Participants earned a bonus each weekend for completing all 12 calls, and those who completed all 60 calls during the study earned $250.


Our dependent variable was presence of MDD as determined by psychiatric diagnosis. Our independent variables were media exposures as estimated by EMA. Participants were asked at every telephone call to identify any media they were using. They were asked specifically about the following 6 types of media: (1) television or movies, (2) music, (3) video games, (4) Internet, (5) magazines or newspapers, and (6) books. Each media type was coded as yes vs no for each call. For each individual, we computed the percentage of calls on which he or she was using each of these types of media. Because there were so few individuals who used magazines and/or newspapers, we combined these data with books into a single print media category. It was possible for participants to be using 1 or more of these media types at the same time. For example, if a participant was watching television on the Internet (via a site such as http://www.hulu.com), the television or movies and Internet categories were each coded as yes.


We first compared the percentage of calls on which participants were exposed to a given media type using Mann-Whitney tests because media exposure variables were not normally distributed. We then developed multivariable models using logistic regression to determine the independent association of each independent variable and MDD controlling for age, sex, and race. Age was represented in years as a continuous variable. Race compared nonwhite with white participants. For multivariable analyses, independent variables were grouped into 4 quartiles based on the amount of media use. This was performed for ease of interpretation; summary ORs represented the odds of having depression for each increasing quartile of use. We defined statistical significance with a 2-tailed α = .05 a priori.

We conducted exploratory analyses that included interaction terms between (1) age and any of the media exposures and (2) sex and any of the media exposures in our regression models. These analyses investigated whether associations between independent and dependent variables may have differed by age and/or sex.

Not every participant was reached on every telephone call. Because there were 106 individuals, there were a total of 6360 possible calls. In total, 723 calls (11.4%) were not completed and 5637 (88.6%) were completed. Compared with calls with individuals without MDD, calls with participants with MDD were more likely to be missed (13.5% vs 9.7%; P < .001). However, because the predictor variables ultimately used were based on the proportion of calls during which students were reached, there were no missing data for these variables.


Most of the 106 study participants were white (88.7%); the remainder were black, Hispanic, or biracial. Most (63.2%) were female, and the mean age was 12.7 (range, 7-17; SD, 2.7) years. Age was not significantly different between those with and without MDD (12.8 vs 12.6 years; P = .67). Sex was also not significantly different between those with and without MDD (38 [63.3%] vs 29 [63.0%] female; P = .98). However, nonwhite participants in the sample were more likely to have MDD than their white counterparts (10 [21.7%] vs 2 [3.3%]; P = .003).

The average participant was watching a movie or television on 26.3% of calls (SD, 13.7%). Other mean media exposures were as follows: 9.1% (SD, 8.5%) for audio, 6.1% (SD, 6.6%) for Internet, 6.0% (SD, 8.1%) for video games, and 0.2% (SD, 0.3%) for reading nonelectronic media.

In bivariable analyses, those with MDD were more commonly watching television or movies than those without MDD (29.6% vs 23.9%; P = .04). There also was a trend toward more audio exposure in those with MDD (10.2% vs 8.4%; P = .06). Other comparisons were nonsignificant and are shown in the Table.

Media Exposure by Presence of MDD
Media Exposure by Presence of MDD

In multivariable models controlling for age, sex, and race, increasing exposure to television or movies was not associated with increased likelihood of having MDD (OR, 1.2 for each increasing quartile of use; 95% CI, 0.8-1.7; P = .37 for trend). However, audio use was significantly associated with having MDD (OR, 1.8 for each increasing quartile of exposure; 95% CI, 1.1-2.8; P = .01 for trend). Use of print media was significantly inversely associated with having MDD (OR, 0.5 for each increasing quartile of use; 95% CI, 0.3-0.9; P = .009 for trend). Other associations are shown in the Table.

Exploratory analyses revealed no interaction terms between age or sex and any of the media exposures when multivariable logistic regressions models were built with presence of MDD as the dependent variable (data not shown; P > .05 for all comparisons).


In this study using EMA to measure media exposure, we found that increasing use of audio-based media (eg, MP3 players and compact disc players) was linearly associated with increasing likelihood of MDD diagnosis. We also found a negative association between reading print material and MDD. Although point estimates suggested weak positive associations of MDD with other electronic media exposures, these associations were not statistically significant.

Lower nonelectronic media use among those with MDD is consistent with other work in this area, which found book exposure during adolescence to be inversely associated with development of MDD by young adulthood.10 The use of the more rigorous EMA assessment technique in the present investigation supports and strengthens that finding. Because the previous study was longitudinal, it suggests that reading may somehow subsequently protect against MDD.10 However, it may also be that those with MDD tend to lack the concentration and/or motivation to read, which requires more cognitive energy than consumption of other media.23-25

That same large study found that television exposure was associated with MDD,10 whereas the present study found that an initially significant bivariable association between television or movie exposure and MDD did not persist after correction for sociodemographic variables. The previous study was longitudinal; thus, it is possible that this association is not present initially but that, over time, increased television exposure may become more strongly associated with MDD. However, because the present study used a more rigorous EMA-based measurement of media exposure, it is also possible that the previous study, which relied on participant self-report, may have been affected by recall bias and that the hypothesized association between television exposure and depression may not be as strong as suggested.

After adjustment for sociodemographic variables, audio (music) exposure had a strong linear association with MDD, but television exposure did not bear this association. Association of music exposure with MDD has been hypothesized. For example, sadness is a common theme in popular music, and it may be that individuals with depression turn to these messages to make themselves feel less alone in their sadness.43-46 Conversely, it may also be that individuals with MDD turn to happy music to “tune out” their negative moods or to elevate their moods.47 Other researchers have suggested that heavy exposure to the sometimes dark themes of popular music may contribute to the development of conditions such as MDD.43-46 Whatever the mechanism, this empirical study suggests that this association may be stronger than previously considered.

Although MDD was not significantly associated with Internet or video game use, there was a nonsignificant trend toward higher use/exposure to video games among those with MDD. Previous studies30,48 have demonstrated associations between depression and video games in adolescents, and a recent study49 linked video game use with MDD in women. The authors of the last study noted that women may self-medicate for depression by “losing themselves” in virtual game worlds.49 It is also possible that women may be particularly vulnerable to stereotypical and/or otherwise negative portrayals of female characters in video games.17,50

Web-based media may be processed similarly to print,51 suggesting that computer use (much of which is Web based) and print media use may have similar associations with MDD. However, more recent work52 highlights the fact that individuals tend to selectively scan Web content rather than absorbing it more fully, suggesting that in other ways Web use may be more similar to television use. Our results—which did not find statistically significant associations between Internet use and MDD—are consistent with the Web as a possible amalgamation of print- and television-type content.

Recruitment for this study focused on a particular geographic region and was not randomly generated; therefore, these results are not necessarily broadly generalizable. The cross-sectional method also precludes inferences about the directionality of the associations observed herein. Future studies will need to more specifically assess the hypothesized relationships in either direction. Another limitation is related to the EMA method, which relies on probability to accurately measure media exposures. Although in general we would expect that EMA-based estimates of media exposure will correspond to actual exposure and be less error prone than self-reported data, any intermittent sampling method may be subject to error.

In addition, our study used types of media exposures as independent variables without assessing specific types of media use (eg, Internet use for real-time social networking vs browsing) and/or contextual elements of use (eg, whether television viewing occurs with friends, family, or alone). Future research in this area may find it valuable to use more nuanced independent variables. Two final limitations involve the outcome variable (psychiatric diagnosis). Of our participants with depression, 74% had a comorbid anxiety disorder. Thus, owing to the high occurrence of both disorders within individuals, it may be hard to tease apart the associations between depression and media use from those between anxiety and media use. Finally, although we used an intensive method to determine diagnoses, we were not able to update this process during the 8-week period of the study, suggesting that diagnosis could have changed during that time, particularly if youth improved in treatment. However, this limitation should have the effect of biasing our findings toward the null hypothesis (because of regression to the mean and potential improvements in depression). Therefore, it does not diminish our credence in the major findings noted in this study.


Despite its limitations, this study provides valuable insight into potential associations of media exposure with MDD. In particular, it lends additional support to previous theoretical concepts and empirical studies linking MDD with increased popular music use and decreased print media use. Although this study helps clarify some of the theoretical relationships of our conceptual model, it also suggests the need to further elucidate the directionality and strength of those relationships. Determining the potential role of media use in the development or perpetuation of MDD may lead to interventions designed to better screen for and prevent MDD.

Correspondence: Brian A. Primack, MD, EdM, MS, Division of General Internal Medicine, Department of Medicine, University of Pittsburgh, 230 McKee Pl, Ste 600, Pittsburgh, PA 15213 (bprimack@pitt.edu).

Accepted for Publication: November 24, 2010.

Author Contributions: Dr Primack had full access to all 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: Primack, Silk, Shadel, Dahl, and Switzer. Acquisition of data: Primack, Silk, and Dahl. Analysis and interpretation of data: Primack, Silk, DeLozier, Dillman Carpentier, and Switzer. Drafting of the manuscript: Primack and DeLozier. Critical revision of the manuscript for important intellectual content: Primack, Silk, Shadel, Dillman Carpentier, Dahl, and Switzer. Statistical analysis: Primack, Silk, Dillman Carpentier, and Dahl. Obtained funding: Primack, Silk, and Shadel. Administrative, technical, and material support: Silk and DeLozier. Study supervision: Primack and Switzer.

Financial Disclosure: None reported.

Funding/Support: This study was supported in part by research grant R01-CA140150 from the National Cancer Institute, career development award K07-CA114315 from the National Cancer Institute, and a grant from the Maurice Falk Foundation (Dr Primack); by grant P01-MH41712 from the National Institute of Mental Health (Neal D. Ryan, MD [principal investigator], and Dr Dahl [coprincipal investigator]); and by the RAND-University of Pittsburgh Health Institute (http://www.ruphi.pitt.edu/), a formal collaboration among the RAND Corporation, RAND Health, and the University of Pittsburgh Schools of the Health Sciences.

Lopez  ADMathers  CDEzzati  MJamison  DTMurray  CJ Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data.  Lancet 2006;367 (9524) 1747- 1757PubMedGoogle Scholar
Blazer  DGKessler  RC McGonagle  KASwartz  MS The prevalence and distribution of major depression in a national community sample: the National Comorbidity Survey.  Am J Psychiatry 1994;151 (7) 979- 986PubMedGoogle Scholar
Commission on Adolescent Depression and Bipolar Disorder, Treating and Preventing Adolescent Mental Health Disorders: What We Know and What We Don't Know: A Research Agenda for Improving the Mental Health of Our Youth.  New York, NY Oxford University Press2005;
Paradis  ADReinherz  HZGiaconia  RMFitzmaurice  G Major depression in the transition to adulthood: the impact of active and past depression on young adult functioning.  J Nerv Ment Dis 2006;194 (5) 318- 323PubMedGoogle Scholar
Reinherz  HZGiaconia  RMHauf  AMWasserman  MSSilverman  AB Major depression in the transition to adulthood: risks and impairments.  J Abnorm Psychol 1999;108 (3) 500- 510PubMedGoogle Scholar
Bohnert  AMGarber  J Prospective relations between organized activity participation and psychopathology during adolescence.  J Abnorm Child Psychol 2007;35 (6) 1021- 1033PubMedGoogle Scholar
Heponiemi  TElovainio  MKivimäki  MPulkki  LPuttonen  SKeltikangas-Järvinen  L The longitudinal effects of social support and hostility on depressive tendencies.  Soc Sci Med 2006;63 (5) 1374- 1382PubMedGoogle Scholar
Rideout  VFoehr  URoberts  D Generation M2: Media in the Lives of 8- to 18-Year-Olds.  Menlo Park, CA Kaiser Family Foundation2010;
Lenhart  APurcell  KSmith  AZickuhr  K Social Media and Young Adults.  Washington, DC Pew Internet2010;
Primack  BASwanier  BGeorgiopoulos  AMLand  SRFine  MJ Association between media use in adolescence and depression in young adulthood: a longitudinal study.  Arch Gen Psychiatry 2009;66 (2) 181- 188PubMedGoogle Scholar
McHale  SMCrouter  ACTucker  CJ Free-time activities in middle childhood: links with adjustment in early adolescence.  Child Dev 2001;72 (6) 1764- 1778PubMedGoogle Scholar
Primack  BAKraemer  KLFine  MJDalton  MA Media exposure and marijuana and alcohol use among adolescents.  Subst Use Misuse 2009;44 (5) 722- 739PubMedGoogle Scholar
Carpentier  FRBrown  JDBertocci  MSilk  JSForbes  EEDahl  RE Sad kids, sad media? applying mood management theory to depressed adolescents' use of media.  Media Psychol 2008;11 (1) 143- 166PubMedGoogle Scholar
Becker  AEBurwell  RAGilman  SEHerzog  DBHamburg  P Eating behaviours and attitudes following prolonged exposure to television among ethnic Fijian adolescent girls.  Br J Psychiatry June2002;180509- 514PubMedGoogle Scholar
Pfefferbaum  BNixon  SJTucker  PM  et al.  Posttraumatic stress responses in bereaved children after the Oklahoma City bombing.  J Am Acad Child Adolesc Psychiatry 1999;38 (11) 1372- 1379PubMedGoogle Scholar
Rich  MBar-On  M Child health in the information age: media education of pediatricians.  Pediatrics 2001;107 (1) 156- 162PubMedGoogle Scholar
Strasburger  VCWilson  BJJordan  AB Children, Adolescents, and the Media. 2nd ed. Thousand Oaks, CA Sage Publications2009;
Christakis  DAZimmerman  FJ Violent television viewing during preschool is associated with antisocial behavior during school age.  Pediatrics 2007;120 (5) 993- 999PubMedGoogle Scholar
Lewinsohn  PMRohde  PSeeley  JR Major depressive disorder in older adolescents: prevalence, risk factors, and clinical implications.  Clin Psychol Rev 1998;18 (7) 765- 794PubMedGoogle Scholar
Eisend  MMoller  J The influence of TV viewing on consumers' body images and related consumption behavior.  Mark Lett 2007;18 (1-2) 101- 116Google Scholar
Lakdawalla  ZHankin  BLMermelstein  R Cognitive theories of depression in children and adolescents: a conceptual and quantitative review.  Clin Child Fam Psychol Rev 2007;10 (1) 1- 24PubMedGoogle Scholar
Harrision  KCantor  J Tales from the screen: enduring fright reactions to scary media.  Media Psychol 1999;1 (2) 97- 116Google Scholar
DeFleur  MLDavenport  LCronin  MDeFleur  M Audience recall of news presented by newspaper, computer, television and radio.  Journal Q 1992;69 (4) 1010- 1022Google Scholar
Faccoro  LDeFleur  ML A cross-cultural experiment on how well audiences remember news stories from newspaper, computer, television and radio sources.  Journal Q 1993;70 (3) 585- 601Google Scholar
Grabe  MEKamhawi  RYegiyan  N Informing citizens: how people with different levels of education process television, newspaper, and web news.  J Broadcast Electron Media 2009;53 (1) 90- 111Google Scholar
Zillmann  D Mood management in the context of selective exposure theory. Roloff  MF Communication Yearbook 23. Thousand Oaks, CA Sage Publications2000;103- 123Google Scholar
Mares  M-LCantor  J Elderly viewers' responses to televised portrayals of old age: empathy and mood management versus social comparison.  Communic Res 1992;19 (4) 459- 478Google Scholar
Oliver  MBWeaver  JBSargent  S An examination of factors related to sex differences in enjoyment of sad films.  J Broadcast Electron Media 2000;44 (2) 282- 300Google Scholar
Nabi  RLFinnerty  KDomschke  THull  S Does misery love company? exploring the therapeutic effects of TV viewing on regretted experiences.  J Commun 2006;56 (4) 689- 706Google Scholar
Mathers  MCanterford  LOlds  THesketh  KRidley  KWake  M Electronic media use and adolescent health and well-being: cross-sectional community study.  Acad Pediatr 2009;9 (5) 307- 314PubMedGoogle Scholar
Epstein  DHPreston  KL Daily life hour by hour, with and without cocaine: an ecological momentary assessment study.  Psychopharmacology (Berl) 2010;211 (2) 223- 232PubMedGoogle Scholar
Haedt-Matt  AAKeel  PK Hunger and binge eating: a meta-analysis of studies using ecological momentary assessment [published online November 5, 2010].  Int J Eat Disord 2010;PubMed10.1002/eat.20868Google Scholar
Kanning  MSchlicht  W Be active and become happy: an ecological momentary assessment of physical activity and mood.  J Sport Exerc Psychol 2010;32 (2) 253- 261PubMedGoogle Scholar
Kashdan  TBFerssizidis  PCollins  RLMuraven  M Emotion differentiation as resilience against excessive alcohol use: an ecological momentary assessment in underage social drinkers.  Psychol Sci 2010;21 (9) 1341- 1347PubMedGoogle Scholar
Wenze  SJMiller  IW Use of ecological momentary assessment in mood disorders research.  Clin Psychol Rev 2010;30 (6) 794- 804PubMedGoogle Scholar
Silk  JForbes  EWhalen  D  et al.  Understanding daily emotional and social dynamics in depressed youth: a cell-phone ecological momentary assessment study [published online November 26, 2010].  J Exp Child Psychol PubMed10.1016/j.jecp.2010.10.007Google Scholar
Kessler  RCAvenevoli  SRies Merikangas  K Mood disorders in children and adolescents: an epidemiologic perspective.  Biol Psychiatry 2001;49 (12) 1002- 1014PubMedGoogle Scholar
American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders, Revised. 3rd ed. Washington, DC American Psychiatric Association1987;
American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders. 4th ed. Washington, DC American Psychiatric Association1994;
Birmaher  BBridge  JAWilliamson  DE  et al.  Psychosocial functioning in youths at high risk to develop major depressive disorder.  J Am Acad Child Adolesc Psychiatry 2004;43 (7) 839- 846PubMedGoogle Scholar
Birmaher  BDahl  REWilliamson  DE  et al.  Growth hormone secretion in children and adolescents at high risk for major depressive disorder.  Arch Gen Psychiatry 2000;57 (9) 867- 872PubMedGoogle Scholar
Kaufman  JBirmaher  BBrent  D  et al.  Schedule for Affective Disorders and Schizophrenia for School-Age Children–Present and Lifetime Version (K-SADS-PL): initial reliability and validity data.  J Am Acad Child Adolesc Psychiatry 1997;36 (7) 980- 988PubMedGoogle Scholar
Baker  FBor  W Can music preference indicate mental health status in young people?  Australas Psychiatry 2008;16 (4) 284- 288PubMedGoogle Scholar
Chen  MJMiller  BAGrube  JWWaiters  ED Music, substance use, and aggression.  J Stud Alcohol 2006;67 (3) 373- 381PubMedGoogle Scholar
Keen  AW Using music as a therapy tool to motivate troubled adolescents.  Soc Work Health Care 2004;39 (3-4) 361- 373PubMedGoogle Scholar
North  ACHargreaves  DJ Problem music and self-harming.  Suicide Life Threat Behav 2006;36 (5) 582- 590PubMedGoogle Scholar
Knobloch  SZillmann  D Mood management via the digital jukebox.  J Commun 2002;52 (2) 351- 366Google Scholar
Subrahmanyam  KKraut  REGreenfield  PMGross  EF The impact of home computer use on children's activities and development.  Future Child 2000;10 (2) 123- 144PubMedGoogle Scholar
Weaver  JB  IIIMays  DSargent Weaver  S  et al.  Health-risk correlates of video-game playing among adults.  Am J Prev Med 2009;37 (4) 299- 305PubMedGoogle Scholar
Primack  BA Video games: play or “playlike activity”?  Am J Prev Med 2009;37 (4) 379- 380PubMedGoogle Scholar
Greenwald  AGLeavitt  C Audience involvement in advertising: four levels.  J Consum Res 1984;11 (1) 581- 592Google Scholar
Eveland  WPDunwoody  S An investigation of elaboration and selective scanning as mediators of learning from the Web versus print.  J Broadcast Electron Media 2002;46 (1) 34- 53Google Scholar