title: Seasonality in seeking mental health information on Google.
abstract: BACKGROUND
  Population mental health surveillance is an important challenge limited by resource constraints, long time lags in data collection, and stigma. One promising approach to bridge similar gaps elsewhere has been the use of passively generated digital data.
  
  
  OBJECTIVE
  This article assesses the viability of aggregate Internet search queries for real-time monitoring of several mental health problems, specifically in regard to seasonal patterns of seeking out mental health information.
  
  
  METHODS
  All Google mental health queries were monitored in the U.S. and Australia from 2006 to 2010. Additionally, queries were subdivided among those including the terms ADHD (attention deficit-hyperactivity disorder); anxiety; bipolar; depression; anorexia or bulimia (eating disorders); OCD (obsessive-compulsive disorder); schizophrenia; and suicide. A wavelet phase analysis was used to isolate seasonal components in the trends, and based on this model, the mean search volume in winter was compared with that in summer, as performed in 2012.
  
  
  RESULTS
  All mental health queries followed seasonal patterns with winter peaks and summer troughs amounting to a 14% (95% CI=11%, 16%) difference in volume for the U.S. and 11% (95% CI=7%, 15%) for Australia. These patterns also were evident for all specific subcategories of illness or problem. For instance, seasonal differences ranged from 7% (95% CI=5%, 10%) for anxiety (followed by OCD, bipolar, depression, suicide, ADHD, schizophrenia) to 37% (95% CI=31%, 44%) for eating disorder queries in the U.S. Several nonclinical motivators for query seasonality (such as media trends or academic interest) were explored and rejected.
  
  
  CONCLUSIONS
  Information seeking on Google across all major mental illnesses and/or problems followed seasonal patterns similar to those found for seasonal affective disorder. These are the first data published on patterns of seasonality in information seeking encompassing all the major mental illnesses, notable also because they likely would have gone undetected using traditional surveillance.
authors.affiliation: Center for Behavioral Epidemiology and Community Health, San Diego, CA 92123, USA. ayers.john.w@gmail.com
authors.forename: John W
authors.initials: JW
authors.lastname: Ayers
authors.forename: Benjamin M
authors.initials: BM
authors.lastname: Althouse
authors.forename: Jon-Patrick
authors.initials: JP
authors.lastname: Allem
authors.forename: J Niels
authors.initials: JN
authors.lastname: Rosenquist
authors.forename: Daniel E
authors.initials: DE
authors.lastname: Ford
mesh_terms: D001289:Attention Deficit Disorder with Hyperactivity; D001315:Australia; D001714:Bipolar Disorder; D001068:Feeding and Eating Disorders; D006801:Humans; D057226:Information Seeking Behavior; D020407:Internet; D001523:Mental Disorders; D008603:Mental Health; D009771:Obsessive-Compulsive Disorder; D012559:Schizophrenia; D057386:Search Engine / Q000706:statistics & numerical data*; D012621:Seasons; D014481:United States; D058067:Wavelet Analysis