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Personalized Media: A Genetically Informative Investigation of Individual Differences in Online Media Use.

ABSTRACT: Online media use has become an increasingly important behavioral domain over the past decade. However, studies into the etiology of individual differences in media use have focused primarily on pathological use. Here, for the first time, we test the genetic influences on online media use in a UK representative sample of 16 year old twins, who were assessed on time spent on educational (N = 2,585 twin pairs) and entertainment websites (N = 2,614 twin pairs), time spent gaming online (N = 2,635 twin pairs), and Facebook use (N = 4,333 twin pairs). Heritability was substantial for all forms of online media use, ranging from 34% for educational sites to 37% for entertainment sites and 39% for gaming. Furthermore, genetics accounted for 24% of the variance in Facebook use. Our results support an active model of the environment, where young people choose their online engagements in line with their genetic propensities.

SUBMITTER: Ayorech Z

PROVIDER: S-EPMC5256859 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Personalized Media: A Genetically Informative Investigation of Individual Differences in Online Media Use.

Ayorech Ziada Z von Stumm Sophie S Haworth Claire M A CM Davis Oliver S P OS Plomin Robert R

PloS one 20170123 1

Online media use has become an increasingly important behavioral domain over the past decade. However, studies into the etiology of individual differences in media use have focused primarily on pathological use. Here, for the first time, we test the genetic influences on online media use in a UK representative sample of 16 year old twins, who were assessed on time spent on educational (N = 2,585 twin pairs) and entertainment websites (N = 2,614 twin pairs), time spent gaming online (N = 2,635 tw ...[more]

PMID: 28114386

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Project description:The often-used A(C)E model that decomposes phenotypic variance into parts due to additive genetic and environmental influences can be extended to a longitudinal model when the trait has been assessed at multiple occasions. This enables inference about the nature (e.g., genetic or environmental) of the covariance among the different measurement points. In the case that the measurement of the phenotype relies on self-report data (e.g., questionnaire data), often, aggregated scores (e.g., sum-scores) are used as a proxy for the phenotype. However, earlier research based on the univariate ACE model that concerns a single measurement occasion has shown that this can lead to an underestimation of heritability and that instead, one should prefer to model the raw item data by integrating an explicit measurement model into the analysis. This has, however, not been translated to the more complex longitudinal case. In this paper, we first present a latent state twin A(C)E model that combines the genetic twin model with an item response theory (IRT) model as well as its specification in a Bayesian framework. Two simulation studies were conducted to investigate 1) how large the bias is when sum-scores are used in the longitudinal A(C)E model and 2) if using the latent twin model can overcome the potential bias. Results of the first simulation study (e.g., AE model) demonstrated that using a sum-score approach leads to underestimated heritability estimates and biased covariance estimates. Surprisingly, the IRT approach also lead to bias, but to a much lesser degree. The amount of bias increased in the second simulation study (e.g., ACE model) under both frameworks, with the IRT approach still being the less biased approach. Since the bias was less severe under the IRT approach than under the sum-score approach and due to other advantages of latent variable modelling, we still advise researcher to adopt the IRT approach. We further illustrate differences between the traditional sum-score approach and the latent state twin A(C)E model by analyzing data of a two-wave twin study, consisting of the answers of 8,016 twins on a scale developed to measure social attitudes related to conservatism.

Project description:BackgroundDepression is already highly prevalent by late adolescence, indicating that research into its developmental emergence should consider earlier risk factors and environmental contexts. The home environment is a key context for children and adolescents throughout development. However, the nature of relationships that exist between aspects of the home environment and the development of depressive symptoms cannot be assumed. Genetically informative studies have been used to provide insights about the aetiology of such relationships, often finding them to be partly confounded by the influence of children's genes. Here, we investigate developmental change in the aetiology of the association between aspects of the home environment and depressive symptoms at the onset of adolescence.MethodsWe used longitudinal child- and parent-report data from >5,000 twin pairs enrolled in the UK-representative Twins Early Development Study. Multivariate, genetically sensitive structural equation models were used to decompose latent variance and covariance in depressive symptoms (measured at 12 and 16 years) and aspects of the home environment (at 9 and 14 years) into genetic and environmental influences.ResultsGoing from childhood to adolescence, genetic influences accounted for an increasing proportion of the association [30% (16-42) of r = .44 in childhood; 40% (25-61) of r = .43 in adolescence], at the expense of shared environmental influences, which decreased from 70% (58-83) to 48% (29-62). Unique environmental influences accounted for a significant proportion of the association in adolescence only [12% (06-18)]. Developmental changes could largely be attributed to subtle shifts in the relative importance of stable aetiological factors, rather than the emergence of influences unique to adolescence.ConclusionsThese findings emphasise the importance of developmental and aetiological context in interpreting associations between aspects of the home environment and child emotional outcomes.

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Personalized Media: A Genetically Informative Investigation of Individual Differences in Online Media Use.

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Personalized Media: A Genetically Informative Investigation of Individual Differences in Online Media Use.

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