Additional file 1: of Effectiveness and acceptance of a web-based depression intervention during waiting time for outpatient psychotherapy: study protocol for a randomized controlled trial

SPIRIT-checklist. (DOC 162 kb

Study inclusion meta-analysis: flow chart.

<p>Study inclusion meta-analysis: flow chart.</p

Effects of low-intensity Internet alcohol treatment on alcohol consumption in comparison with no-intervention controls, and subgroup analyses of associations between effect sizes and study characteristics (Hedges’s <i>g</i>).

<p><i>Notes:</i><b>bold</b> – near significance;^a-1 according to the random effects model;^a-2: according to the mixed effects model ^bThe P-values in this column indicatewhether the Q-statistic is significant (I2-statistics do not include a test of significance).; ^cThe P-values in this column indicate whether the difference between the effect sizes in the subgroups is significant.; *P≤0.05; **P<0.01; ***P≤0.001; AUDIT: Alcohol Use Disorders Identification Test; CBT cognitive-behavioural therapy; CI = confidence interval; CO completers-only analysis; DSM = Diagnostic Statistical Manual of Mental Disorders; FAST: Fast Alcohol Screening Test; ITT intention-to-treat analysis; MI motivational interviewing; n comp = number of comparisons; NNT = number needed to treat; PNF personalised normative feedback.</p

Results of meta-analysis: forest plot.

<p>Results of meta-analysis: forest plot.</p

Additional file 1: of Effectiveness of a transdiagnostic individually tailored Internet-based and mobile-supported intervention for the indicated prevention of depression and anxiety (ICare Prevent) in Dutch college students: study protocol for a randomised controlled trial

A populated SPIRIT checklist. (PDF 95 kb

Additional file 2: of Effectiveness of a transdiagnostic individually tailored Internet-based and mobile-supported intervention for the indicated prevention of depression and anxiety (ICare Prevent) in Dutch college students: study protocol for a randomised controlled trial

SPIRIT figure. (PDF 28 kb

Time series of Ebola-related news media, Twitter, and Google search data used in this study.

<p>The samples consist of six weeks of data ending October 31^st, 2014. The first case of Ebola confirmed in the U. S. occurred on September 29, 2014. The temporal trends in the data samples are highly inter-correlated, with a minimum of 70% correlation between samples.</p

Fits of the news media contagion model, and a simple linear regression model, to the sources of data used in this study.

<p>The fits of the linear regression model (shown in blue) tend to be generally too low in the beginning and too high at the end. In contrast, the contagion model (red line) accounts for the boredom effect, where people become more and more disinclined to perform Ebola-related searches or tweets after an extended period of exposure to Ebola-related news-coverage. Incorporation of this dynamic in the model yields significantly better fits to the data compared to the regression model.</p

Parameters of the Ebola-related news media contagion model of Eq 2 or Eq 3 (as appropriate to the sample), fit to the Ebola-related Google searches and tweets.

<p>The parameter <i>f</i> is the initial fraction of the population susceptible to news media induced Ebola interest or panic (as manifested by the particular Ebola-related Internet searches or tweets in our samples). The parameter <i>β</i> is the transmission rate, and 1/<i>γ</i> is the average time, in days between an individual viewing an Ebola-related news video, and performing an Ebola-related Google search or tweet. The average number of particular Internet searches or tweets in our samples inspired by a single news video in the initial susceptible population is <i>fβ</i>. The numbers in the square brackets represent the 95% confidence intervals.</p><p>Parameters of the Ebola-related news media contagion model of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129179#pone.0129179.e002" target="_blank">Eq 2</a> or <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129179#pone.0129179.e003" target="_blank">Eq 3</a> (as appropriate to the sample), fit to the Ebola-related Google searches and tweets.</p

The percentage of the variance, <i>R</i>², of the data samples described by the contagion model of Eq 1, assuming that the news videos, <i>V</i>, cause the patterns seen in the data (<i>V</i> → <i>I</i>).

<p>Also shown are the <i>R</i>² under the assumption that the temporal patterns in the data samples cause the temporal patterns in the news videos (<i>I</i> → <i>V</i>). The p-values testing for Granger causality between the various time series are also shown.</p><p>The percentage of the variance, <i>R</i>², of the data samples described by the contagion model of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129179#pone.0129179.e001" target="_blank">Eq 1</a>, assuming that the news videos, <i>V</i>, cause the patterns seen in the data (<i>V</i> → <i>I</i>).</p