The impact of digital media on children’s intelligence while controlling for genetic differences in cognition and socioeconomic background

May combine two claims.

In the sibling analysis, the pattern of correlations between baseline intelligence and each type of screen time remained the same as in the main models: negative correlations of intelligence with Watching and Socializing, and no correlation with Gaming.

“In this analysis, the pattern of correlations between baseline intelligence and each type of screen time remained the same as we found in the main models reported above. That is: negative correlations of intelligence with Watching and Socializing, and no correlation with Gaming.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Boys spent slightly more time Watching than girls (boys mean = 2.35, SE = 0.02; girls mean = 2.18, SE = 0.03).

“Boys also spent slightly (and significantly) more time than girls Watching [boys mean = 2.35 (SE = 0.02), girls mean = 2.18 (SE = 0.03)].”

Probably not a substantial claim: may not pass S3 (load-bearing).

In posthoc analysis with parental education, the association of parental education to baseline intelligence was significant 0.32.

“The association of parental education to baseline intelligence was a significant 0.32 (while SES in the original model was a significant 0.24).”

May combine two claims.

Watching and Socializing were negatively correlated with intelligence at baseline when children were 9-10 years old, while Gaming showed no association.

“Watching and Socializing were negatively correlated with intelligence at baseline measures when children were 9–10 years old, while Gaming showed no association.”

May combine two claims.

In posthoc analysis splitting Watching into TV and online video, baseline intelligence had independent associations with TV watching (β = −0.09, p < 0.001) and online video watching (β = −0.07, p < 0.001).

“As expected, baseline intelligence at age 9–10 had independent, positive associations with TV watching (β = − 0.09, p < 0.001) and online video watching (β = − 0.07, p < 0.001).”

May combine two claims.

In all three mediation models, lack of perseverance had a negative association with intelligence at age 9 (all β = −0.08, all p < 0.001), but no effect on the change in intelligence at follow-up (all β = 0.08, all p > 0.138).

“In all three models, lack of perseverance had a negative association with intelligence at age 9 (all β = − 0.08, all p < 0.001), but no effect on the change in intelligence at follow-up 2 years later (all β = 0.08, all p > 0.138).”

May combine two claims.

In posthoc analysis splitting Watching, there was no significant effect on the change in intelligence from TV watching (β = 0.06, p = 0.246) nor from online video watching (β = 0.09, p = 0.139).

“With these two variables split, now there was no significant effect on the change in intelligence from TV watching (β = 0.06, p = 0.246) nor from online video watching (β = 0.09, p = 0.139).”

May combine two claims.

CogPGS and SES showed positive associations with each other and with baseline measures of intelligence at 9-10 years old.

“CogPGS and SES showed positive associations with each other and with the baseline measures of intelligence at 9–10 years old, in agreement with past research on children and teenagers45–47.”

• cites [45] Paudel, S., Jancey, J., Subedi, N. & Leavy, J. Correlates of mobile screen media use among children aged 0–8: A systematic review. BMJ Open 7, e014585 (2017). sourceNot checked yet

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• cites [46] Judd, N. et al. Cognitive and brain development is independently influenced by socioeconomic status and polygenic scores for educational attainment. Proc. Natl. Acad. Sci. 117, 12411–12418 (2020). sourceNot checked yet

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• cites [47] Sauce, B., Wiedenhoeft, J., Judd, N. & Klingberg, T. Change by challenge: A common genetic basis behind childhood cognitive development and cognitive training. NPJ Sci. Learn. 6, 16 (2021). sourceNot checked yet

  Not checked in the free run.

May combine two claims.

In sibling analysis, the change in intelligence was not significantly impacted by Watching (β = −0.0008, p = 0.506), Socializing (β = −0.001, p = 0.307), or Gaming (β = −0.001, p = 0.182).

“The change in intelligence was not significantly impacted by Watching (β = − 0.0008, p = 0.506), nor by Socializing (β = − 0.001, p = 0.307), nor by Gaming (β = − 0.001, p = 0.182).”

May combine two claims.

Regarding the change in intelligence, the between-family effect was significant, but the within-family effect was not.

“Regarding the change in intelligence, the between-family effect was significant, but the within-family effect was not.”

May combine two claims.

In sibling analysis with change in intelligence, the within-family effect of cogPGS on the change was not significant (βw = 0.001, p = 0.142) while the between-family effect was significant (βb = 0.004, p < 0.001).

“In the sibling analysis with change in intelligence, the within-family effect of cogPGS on the change in intelligence was not significant (βw = 0.001, p = 0.142) while the between-family effect was significant (βb = 0.004, p < 0.001).”

May combine two claims.

The effect of baseline intelligence at 9 years old on the change in intelligence after two years differed between boys and girls, with a large positive association in boys (β = 0.64, p < 0.001) and no association in girls (β = −0.02, p = 0.866).

“The effect of baseline intelligence at 9 years old on the change in intelligence after two years was significantly different between boys and girls (Chi-square difference = 8.40, p = 0.004), with a very large positive association in boys (β = 0.64, p < 0.001) and no association in girls (β = − 0.02, p = 0.866).”

May combine two claims.

The association between screen time and baseline intelligence differed for Gaming (p = 0.008), with no effect in boys (β = −0.02, p = 0.332) and a positive effect in girls (β = 0.05, p = 0.01).

“That association with baseline intelligence, however, differed for Gaming (Chi-square difference = 6.91, p = 0.008), with no effect in boys (β = − 0.02, p = 0.332) and a positive effect in girls (β = 0.05, p = 0.01), so at age 9–10, girls with higher intelligence tended to play video games slightly more.”

May combine two claims.

The association between screen time and baseline intelligence was not significantly different between boys and girls for Watching (p = 0.052) or Socializing (p = 0.357).

“The association between screen time and baseline intelligence was not significantly different between boys and girls for Watching (Chi-square difference = 3.76, p = 0.052), or for Socializing (Chi-square difference = 0.84, p = 0.357).”

May combine two claims.

Lack of perseverance was correlated with cogPGS (all β = −0.03, all p = 0.003), but not with SES (all p > 0.656).

“All three mediation models had a good fit (all RMSEA < 0.027, all CFI > 0.968), and showed that lack of perseverance was correlated with cogPGS (all β = − 0.03, all p = 0.003), but not with SES (all p > 0.656).”

May combine two claims.

In sibling analysis with baseline intelligence, intelligence had associations with Watching (r = −0.04, p = 0.005) and Socializing (r = −0.04, p < 0.001), but not with Gaming (r = −0.001, p = 0.916).

“In this model with baseline intelligence, the sibling analysis also revealed that intelligence had associations with Watching (r = − 0.04, p = 0.005) and Socializing (r = − 0.04, p < 0.001), but not with Gaming (r = − 0.001, p = 0.916).”

May combine two claims.

CogPGS and SES had no significant impact on the change in intelligence, although gender analyses revealed cogPGS had a very large positive impact on the change in boys but no effect in girls.

“CogPGS and SES had no significant impact on the change in intelligence, although our gender analyses revealed that cogPGS had a very large, positive, impact on the change in boys but no effect in girls.”

May combine two claims.

All screen time types were negatively associated with both cogPGS and SES.

“All screen time types were negatively associated with both cogPGS and SES.”

May combine two claims.

The effect of cogPGS on the change in intelligence differed between boys and girls, with a large positive effect in boys (β = 0.37, p < 0.001) and no effect in girls (β = −0.01, p = 0.915).

“The effect of cogPGS on the change in intelligence was also significantly different between boys and girls (Chi-square difference = 11.59, p < 0.001), with a large positive effect in boys (β = 0.37, p < 0.001) and no effect in girls (β = − 0.01, p = 0.915).”

May combine two claims.

The effect of screen time on the change in intelligence was not significantly different between boys and girls for Watching, Socializing, or Gaming.

“For the effect of screen time on the change in intelligence, it was not significantly different between boys and girls for Watching (Chi-square difference = 0.07, p = 0.788) or for Socializing (Chi-square difference = 0.94, p = 0.331), or for Gaming (Chi-square difference = 1.46, p = 0.226).”

May combine two claims.

Watching also showed a positive effect on the change in intelligence, and Socializing had no effect.

“Surprisingly, Watching also showed a positive effect on the change in intelligence, and, much less shocking, Socializing had no effect.”

May combine two claims.

The positive impact of watching TV and other digital videos on the change in intelligence was less robust than gaming: posthoc analysis replacing SES with parental education showed no impact of watching.

“And such a result was also less robust than gaming: our posthoc analysis replacing SES with parental education showed no impact of watching and the change in intelligence (while the impact still existed for gaming).”

May combine two claims.

The positive effect of Gaming on change in intelligence was also true for Gaming in absolute values and did not differ between boys and girls.

“This was also true for Gaming in absolute values (not correcting for time spent video watching and socializing) and did not differ between boys and girls.”

Probably not a substantial claim: may not pass S3 (load-bearing).

These filtering steps resulted in a cleaned dataset of 10,069 individuals and 430,622 variants.

“These filtering steps resulted in a cleaned dataset of 10,069 individuals and 430,622 variants.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The baseline sample consisted of 9855 participants (M = 9.90 years, SD = 0.62 years, 4686 females).

“We used two different time points: the baseline when children were ages 9–10 ended with a final sample of 9855 participants (M = 9.90 years, SD = 0.62 years, 4686 females), and the follow-up two years later when the children were 11–12 years old had a sample of 5374 participants (M = 11.92 years, SD = 0.63 years, 2512 females).”

May combine two claims.

We found that our measurement across baseline and follow-up had strong measurement invariance (fixed loadings and fixed intercepts from the cognitive tasks between time points), RMSEA = 0.029 and CFI = 0.970.

“We found that our measurement across baseline and follow-up had strong measurement invariance (fixed loadings and fixed intercepts from the cognitive tasks between time points), RMSEA = 0.029 and CFI = 0.970.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The final number of SNPs after imputation was 40,637,119 in a total of 10,069 individuals.

“The final number of SNPs after imputation was 40,637,119 in a total of 10,069 individuals.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The follow-up sample consisted of 5374 participants (M = 11.92 years, SD = 0.63 years, 2512 females).

“We used two different time points: the baseline when children were ages 9–10 ended with a final sample of 9855 participants (M = 9.90 years, SD = 0.62 years, 4686 females), and the follow-up two years later when the children were 11–12 years old had a sample of 5374 participants (M = 11.92 years, SD = 0.63 years, 2512 females).”

Probably not a substantial claim: may not pass S3 (load-bearing).

At the end of this process, we had 5255 SNPs included.

“At the end of this process, we had 5255 SNPs included.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Cross-sectional studies report negative effects of television viewing on attention and learning.

“Cross-sectional studies also report negative effects of television viewing on attention and learning16,17, while a large meta-analysis found that video games do not negatively impact school performance18.”

• cites [16] Johnson, J. G., Cohen, P., Kasen, S. & Brook, J. S. Extensive television viewing and the development of attention and learning difficulties during adolescence. Arch. Pediatr. Adolesc. Med. 161, 480–486 (2007). sourceNot checked yet

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• cites [17] Christakis, D. A., Zimmerman, F. J., DiGiuseppe, D. L. & McCarty, C. A. Early television exposure and subsequent attentional problems in children. Pediatrics 113, 708–713 (2004). sourceNot checked yet

  Not checked in the free run.

May combine two claims.

The contradictions among studies on screen time and cognition are likely due to limitations of cross-sectional designs, relatively small sample sizes, and, most critically, failures to control for genetic predispositions and socio-economic context.

“The contradictions among studies on screen time and cognition are likely due to limitations of cross-sectional designs, relatively small sample sizes, and, most critically, failures to control for genetic predispositions and socio-economic context10.”

• cites [10] Kostyrka-Allchorne, K., Cooper, N. R. & Simpson, A. The relationship between television exposure and children’s cognition and behaviour: A systematic review. Dev. Rev. 44, 19–58 (2017). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

A large meta-analysis found that video games do not negatively impact school performance.

“Cross-sectional studies also report negative effects of television viewing on attention and learning16,17, while a large meta-analysis found that video games do not negatively impact school performance18.”

• cites [18] Drummond, A. & Sauer, J. D. Video-games do not negatively impact adolescent academic performance in science, mathematics or reading. PLoS ONE 9, e87943 (2014). sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

A large longitudinal study found that watching TV and using computers both led to worse school performance, but there were no short- or long-term effects from playing video games.

“A large longitudinal study found that watching TV and using computers both led to worse school performance, but there were no short- or long-term effects from playing video games15.”

• cites [15] Mundy, L. K. et al. Electronic media use and academic performance in late childhood: A longitudinal study. PLoS ONE 15, e0237908 (2020). sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

There are contradictory findings for video games, some with no effects and negative effects.

“Though, like for other digital activities, there are contradictory findings for video games, some with no effects30,31 and negative effects32,33.”

• cites [30] Best, J. R., Miller, P. H. & Jones, L. L. Executive functions after age 5: Changes and correlates. Dev. Rev. 29, 180–200 (2009). sourceNot checked yet

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• cites [31] Ferguson, C. J. The influence of television and video game use on attention and school problems: A multivariate analysis with other risk factors controlled. J. Psychiatr. Res. 45, 808–813 (2011). sourceNot checked yet

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• cites [32] Syväoja, H. J., Tammelin, T. H., Ahonen, T., Kankaanpää, A. & Kantomaa, M. T. The associations of objectively measured physical activity and sedentary time with cognitive functions in school-aged children. PLoS ONE 9, e103559 (2014). sourceNot checked yet

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• cites [33] Swing, E. L., Gentile, D. A., Anderson, C. A. & Walsh, D. A. Television and video game exposure and the development of attention problems. Pediatrics 126, 214–221 (2010). sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

Lack of perseverance is associated with cognitive functions and school achievement.

“Lack of Perseverance is defined as a failure in maintaining focus on a boring or difficult task20 and is associated with cognitive functions21 and school achievement22.”

• cites [21] Gay, P., Rochat, L., Billieux, J., d’Acremont, M. & Van der Linden, M. Heterogeneous inhibition processes involved in different facets of self-reported impulsivity: Evidence from a community sample. Acta Psychol. 129, 332–339 (2008). sourceNot checked yet

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• cites [22] Vettori, G., Vezzani, C., Bigozzi, L. & Pinto, G. Upper secondary school students’ conceptions of learning, learning strategies, and academic achievement. J. Educ. Res. 113, 475–485 (2020). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

In the same cross-sectional study, children in the high tertile for TV watching (β = −0.99), video watching (β = −1.05), and social media (β = −0.79) had lower measures of cognition compared to children in the low tertile.

“However, children in the high tertile for TV watching (β = − 0.99), video watching (β = − 1.05), and social media (β = − 0.79) had lower measures of cognition compared to children in the low tertile for each variable.”

• cites [13] Walsh, J. J., Barnes, J. D., Tremblay, M. S. & Chaput, J.-P. Associations between duration and type of electronic screen use and cognition in US children. Comput. Human Behav. 108, 106312 (2020). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

Time spent with digital devices has increased dramatically since the start of the twenty-first century.

“Time spent with digital devices has increased dramatically since the start of our twenty-first century1,2 and is now a large portion of a child’s free time3.”

• cites [1] Ofcom. Children and Parents: Media Use and Attitudes Report 2015. http://stakeholders.ofcom.org.uk/market-data-research/other/research-publications/childrens/children-parents-nov-15/ (2015).Not checked yet

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• cites [2] Bucksch, J. et al. International Trends in Adolescent Screen-Time Behaviors From 2002 to 2010. J. Adolesc. Health 58, 417–425 (2016). sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

A cross-sectional study found that children who spent more time playing video games (in the high tertile) had a positive association with intelligence compared to low tertile (β = +0.12).

“For example, a cross-sectional study13 found that children who spent more time playing video games (in the high tertile) had a positive association with intelligence compared to low tertile (β = + 0.12).”

• cites [13] Walsh, J. J., Barnes, J. D., Tremblay, M. S. & Chaput, J.-P. Associations between duration and type of electronic screen use and cognition in US children. Comput. Human Behav. 108, 106312 (2020). sourceNot checked yet

  Not checked in the free run.

Classified as probably not a substantial claim.

Different types of screen activities might have distinct effects on intelligence.

“This picture is further complicated because different types of screen activities might have distinct effects on intelligence3,13.”

• cites [3] Adelantado-Renau, M. et al. Association between screen media use and academic performance among children and adolescents: A systematic review and meta-analysis. JAMA Pediatr. 173, 1058–1067 (2019). sourceNot checked yet

  Not checked in the free run.

• cites [13] Walsh, J. J., Barnes, J. D., Tremblay, M. S. & Chaput, J.-P. Associations between duration and type of electronic screen use and cognition in US children. Comput. Human Behav. 108, 106312 (2020). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

Children aged 8-12 spend an average of 4-6 h with a screen each day watching videos, gaming, and socializing.

“Children aged 8–12, for example, spend an average of 4–6 h with a screen each day watching videos, gaming, and socializing1,4.”

• cites [1] Ofcom. Children and Parents: Media Use and Attitudes Report 2015. http://stakeholders.ofcom.org.uk/market-data-research/other/research-publications/childrens/children-parents-nov-15/ (2015).Not checked yet

  Not checked in the free run.

• cites [4] AACAP. Screen Time and Children. https://www.aacap.org/AACAP/Families_and_Youth/Facts_for_Families/FFF-Guide/Children-And-Watching-TV-054.aspx (2020).Not checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

Scores in measures of intelligence are highly predictive of a person's future, including happiness, longevity, income, and educational achievement.

“Scores in measures of intelligence are highly predictive of a person’s future, including happiness, longevity, income, and educational achievement9.”

• cites [9] Deary, I. J., Cox, S. R. & Hill, W. D. Genetic variation, brain, and intelligence differences. Mol. Psychiatry https://doi.org/10.1038/s41380-021-01027-y (2021). sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Intelligence, educational attainment, and other cognitive abilities are all highly heritable.

“This matters because intelligence, educational attainment, and other cognitive abilities are all highly heritable9,34.”

• cites [9] Deary, I. J., Cox, S. R. & Hill, W. D. Genetic variation, brain, and intelligence differences. Mol. Psychiatry https://doi.org/10.1038/s41380-021-01027-y (2021). sourceNot checked yet

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• cites [34] Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

The results are in line with research on malleability of cognitive abilities from environmental factors, such as cognitive training and the Flynn effect.

“Broadly, our results are in line with research on the malleability of cognitive abilities from environmental factors, such as cognitive training and the Flynn effect.”

Classified as probably not a substantial claim. May combine two claims.

The combination of these regions explained 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance.

“The combination of these regions was able to explain 11–13% of the variance in educational attainment and 7–10% of the variance in cognitive performance.”

• cites [34] Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018). sourceNot checked yet

  Not checked in the free run.

May combine two claims.

The measure of lack of perseverance could be less related to intelligence and potentially not matter to the role of screen time on the change after two years.

“Therefore, our measure of lack of perseverance could be less related to intelligence (as shown by the small correlations with cognitive tasks seen in Table 1) and potentially not matter to the role of screen time on the change after two years.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Within the ABCD study, the time spent gaming is described in one variable, including all types of games from very simple smartphone games to complicated multiplayer action games on different consoles.

“Furthermore, within the ABCD study, the time spent gaming is described in one variable, including all types of games from very simple smartphone games to complicated multiplayer action games on different consoles.”

Probably not a substantial claim: may not pass S3 (load-bearing).

We used self-reported measures of screen time.

“Another limitation of our study is that we used self-reported measures of screen time.”

May combine two claims.

Our results regarding genetics will have much higher accuracy and generalizability in white populations.

“This means our results regarding genetics will have much higher accuracy and generalizability in white populations.”

May combine two claims.

The amount of TV shows that children watch is influenced by the education of the parents, and correcting for it results in a lower estimate.

“So, the amount of TV shows that children watch is influenced by the education of the parents, and so correcting for it results in a lower estimate.”

May combine two claims.

The confirmatory factor model fitted the data well (RMSEA = 0.060, CFI = 0.964), meaning the common variance among all five cognitive tasks is well explained by one latent variable called intelligence.

“The confirmatory factor model fitted the data well, with a root-mean-square error of approximation (RMSEA) = 0.060 and a comparative fit index (CFI) = 0.964, which means that the common variance among all five cognitive tasks is well explained by one latent variable that we call here “intelligence”.”

May combine two claims.

Self-reported media use correlates only moderately with logged measurements and self-reports are a somewhat inaccurate reflection of logged (true) media use.

“A recent meta-analysis has found that self-reported media use correlates only moderately with logged measurements and that self-reports are a somewhat inaccurate reflection of logged (true) media use76.”

• cites [76] Parry, D. A. et al. A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use. Nat. Hum. Behav. https://doi.org/10.1038/s41562-021-01117-5 (2021). sourceNot checked yet

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Classified as probably not a substantial claim. May combine two claims.

Alternatively, the amount of TV could merely be correlated with parental education and the impact of video watching is spurious (with parental education being the real cause for intelligence changes).

“Alternatively, the amount of TV could merely be correlated with parental education and the impact of video watching is spurious (with parental education being the real cause for intelligence changes).”

Classified as probably not a substantial claim.

The measure of lack of perseverance is only associated with an unintentional form of interference control rather than with intentionally removing learned material from working memory.

“This result was somewhat surprising, but perhaps it can be explained because our measure of perseverance is only associated with an unintentional form of interference control rather than with intentionally removing learned material from working memory74.”

• cites [74] Rochat, L., Billieux, J., Gagnon, J. & Van der Linden, M. A multifactorial and integrative approach to impulsivity in neuropsychology: Insights from the UPPS model of impulsivity. J. Clin. Exp. Neuropsychol. 40, 45–61 (2018). sourceNot checked yet

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Classified as probably not a substantial claim.

The model also allowed us to estimate the effect of screen time at both baseline values of intelligence at age 9-10 as well as the change in intelligence over two years even after accounting for genetic effects.

“The model also allowed us to estimate the effect of screen time at both baseline values of intelligence at age 9–10 as well as the change in intelligence over two years even after accounting for genetic effects.”

Classified as probably not a substantial claim.

This allowed us to assess the psychometric equivalence of a construct across time.

“This allowed us to assess the psychometric equivalence of a construct across time.”

Classified as probably not a substantial claim. May combine two claims.

The ABCD dataset has performed a set of cognitive tasks and created indices of crystallized intelligence and fluid intelligence.

“The ABCD dataset has performed a set of cognitive tasks and created indices of crystallized intelligence and fluid intelligence.”

Classified as probably not a substantial claim.

According to previous findings and reviews, contrasting the effects of screen time types, we analyzed major screen-based activities separately.

“According to previous findings and reviews3,80, contrasting the effects of screen time types, we analyzed major screen-based activities separately.”

• cites [3] Adelantado-Renau, M. et al. Association between screen media use and academic performance among children and adolescents: A systematic review and meta-analysis. JAMA Pediatr. 173, 1058–1067 (2019). sourceNot checked yet

  Not checked in the free run.

• cites [80] Orben, A. Teenagers, screens and social media: A narrative review of reviews and key studies. Soc. Psychiatry Psychiatr. Epidemiol. 55, 407–414 (2020). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

After imputation, genotypes with an INFO score < 0.3 or a MAF < 0.001% were excluded.

“After imputation, genotypes with an INFO score < 0.3 or a MAF < 0.001% were excluded.”

May combine two claims.

These GWAS had hundreds of thousands of individuals, and such a large sample size allows new studies to detect effects in samples of a few hundred individuals with 80% statistical power.

“Furthermore, these GWAS had hundreds of thousands of individuals, and such a large sample size allows new studies to detect effects in samples of a few hundred individuals with 80% statistical power34.”

• cites [34] Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

The SEM mediation allowed us to test the indirect effect itself by product-of-paths (or difference in coefficients) approach, which cannot usually be done in traditional, regression frameworks.

“The SEM mediation allowed us to test the indirect effect itself by product-of-paths (or difference in coefficients) approach, which cannot usually be done in traditional, regression frameworks.”

Classified as probably not a substantial claim.

As by the Frisch-Waugh-Lovell theorem, such prior covariate residualization from all relevant exogenous and endogenous variables is equivalent to running a regression model with all covariates present.

“As by the Frisch–Waugh–Lovell theorem, such prior covariate residualization from all relevant exogenous and endogenous variables is equivalent to running a regression model with all covariates present (which we couldn’t do here since structural equation modeling cannot handle our large number of covariates).”

Probably not a substantial claim: may not pass S3 (load-bearing).

Written informed consent was obtained from all participants and/or their legal guardians.

“Written informed consent was obtained from all participants and/or their legal guardians.”

Classified as probably not a substantial claim.

Some of the tasks of those indices were not part of the follow-up data collection.

“However, some of the tasks of those indices were not part of the follow-up data collection.”

Classified as probably not a substantial claim.

We kept only one participant at random from each family.

“Therefore, we kept only one participant at random from each family.”

Classified as probably not a substantial claim. May combine two claims.

Such analysis allowed us to compare members (twins and siblings) of the same family and further test the relationship between screen time and intelligence.

“Such analysis allowed us to compare members (twins and siblings) of the same family and further test the relationship between screen time and intelligence.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Effects on cognition depend on genres played, skills required, etc.

“However, from previous studies we know that effects on cognition depend on genres played, skills required, etc.25,77.”

• cites [25] Bediou, B. et al. Meta-analysis of action video game impact on perceptual, attentional, and cognitive skills. Psychol. Bull. 144, 77–110 (2018). sourceNot checked yet

  Not checked in the free run.

• cites [77] Flynn, R. M. & Richert, R. A. Cognitive, not physical, engagement in video gaming influences executive functioning. J. Cogn. Dev. 19, 1–20 (2018). sourceNot checked yet

  Not checked in the free run.

Classified as probably not a substantial claim. May combine two claims.

Before fitting the latent change score model, we assessed fit on respective measurement models, as well as tested for measurement invariance.

“Before fitting the latent change score model, we assessed fit on respective measurement models, as well as tested for measurement invariance.”

Classified as probably not a substantial claim. May combine two claims.

Each original variable used in the models was put in linear regressions with the 20 first principal components of the SNP genotype data to correct for population stratification, as well as with age and site to correct for differences.

“Then, each original variable used in the models was put in linear regressions with the 20 first principal components of the SNP genotype data to correct for population stratification (associations of environmental effects with genetic structure due to geographic separation, population mixtures, and migrations that occurred throughout our evolutionary history)91,92, as well as with the age of the”

• cites [91] Duncan, L. et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat. Commun. 10, 3328 (2019). sourceNot checked yet

  Not checked in the free run.

• cites [92] Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

All research was performed in accordance with relevant guidelines and regulations.

“All research was performed in accordance with relevant guidelines and regulations.”

Classified as probably not a substantial claim.

Recent research has highlighted issues and limitations arising from using SES as a composite measure.

“Recent research has highlighted issues and limitations arising from using SES as a composite measure83.”

• cites [83] Duncan, G. J. & Magnuson, K. Socioeconomic status and cognitive functioning: Moving from correlation to causation. Wiley Interdiscip. Rev. Cogn. Sci. 3, 377–386 (2012). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

We assessed model fit using the comparative fit index (CFI fit > 0.95) and the root-mean-square error of approximation (RMSEA fit < 0.08).

“We assessed model fit using the comparative fit index, (CFI fit > 0.95), and the root-mean-square error of approximation (RMSEA fit < 0.08)98.”

• cites [98] Hu, L. & Bentler, P. M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Modeling 6, 1–55 (1999). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

The research protocol as well as all methods and data collection were reviewed and approved by the central Institutional Review Board (IRB) at the University of California, San Diego.

“The research protocol as well as all methods and data collection were reviewed and approved by the central Institutional Review Board (IRB) at the University of California, San Diego.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Individuals with an absolute autosomal heterozygosity > 0.2 or more than 2% missing genotypes were excluded.

“Moreover, individuals with an absolute autosomal heterozygosity > 0.2 or more than 2% missing genotypes were excluded.”

Classified as probably not a substantial claim.

We chose to use a latent change score model as it allowed us to examine the effect on intelligence from each type of screen time, cogPGS, and SES simultaneously and with reduced measurement error.

“We chose to use a latent change score model as it allowed us to examine the effect on intelligence from each type of screen time, cogPGS, and SES simultaneously and with reduced measurement error93,94.”

• cites [93] McArdle, J. J. Latent variable modeling of differences and changes with longitudinal data. Annu. Rev. Psychol. 60, 577–605 (2009). sourceNot checked yet

  Not checked in the free run.

• cites [94] Henk, C. M. & Castro-Schilo, L. Preliminary detection of relations among dynamic processes with two-occasion data. Struct. Equ. Modeling 23, 180–193 (2016). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Before imputation, SNPs were excluded if they had high levels of missing data (SNP call rate < 98%), departed from Hardy-Weinberg equilibrium (P < 1 × 10 − 6), or had minor allele frequencies (MAF) < 1%.

“Before imputation, SNPs were excluded if they had high levels of missing data (SNP call rate < 98%), departed from Hardy–Weinberg equilibrium as calculated in the lfa R package (sHWE) (P < 1 × 10 − 6), or had minor allele frequencies (MAF) < 1%.”

Classified as probably not a substantial claim.

To check for outliers and to control for population structure the PCA module as implemented in RICOPILI was used.

“To check for outliers and to control for population structure the PCA module as implemented in RICOPILI was used.”

May combine two claims.

The ABCD dataset contains siblings and twins and this information could erroneously alter variance and inflate the precision of our study.

“The ABCD dataset contains siblings and twins and this information could erroneously alter variance and inflate the precision of our study.”

Probably not a substantial claim: may not pass S1 (asserted). May combine two claims.

It might be that parents' education is the subcomponent that matters in shaping children's intelligence, so its impact could be diluted/contaminated when combining it with income and neighborhood quality.

“It might be that parents’ education is the subcomponent that matters in shaping children’s intelligence84,85, so its impact could be diluted/contaminated when combining it with income and neighborhood quality.”

• cites [84] Marks, G. N. & O’Connell, M. Inadequacies in the SES–Achievement model: Evidence from PISA and other studies. Rev. Educ. 9, 3293 (2021). sourceNot checked yet

  Not checked in the free run.

• cites [85] Rindermann, H. & Ceci, S. J. Parents’ education is more important than their wealth in shaping their children’s intelligence: Results of 19 samples in seven countries at different developmental levels. J. Educ. Gift. 41, 298–326 (2018). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

A latent change score model can be conceptualized as a reparameterization of a paired t-test and has recently been highlighted for its usefulness in teasing apart the complex processes involved in longitudinal developmental research.

“A latent change score model can be conceptualized as a reparameterization of a paired t-test and has recently been highlighted for its usefulness in teasing apart the complex processes involved in longitudinal developmental research95,96.”

• cites [95] Coman, E. N. et al. The paired t-test as a simple latent change score model. Front. Psychol. 4, 738 (2013). sourceNot checked yet

  Not checked in the free run.

• cites [96] Kievit, R. A. et al. Developmental cognitive neuroscience using latent change score models: A tutorial and applications. Dev. Cogn. Neurosci. 33, 99–117 (2018). sourceNot checked yet

  Not checked in the free run.

Classified as probably not a substantial claim. May combine two claims.

The Adolescent Brain and Cognitive Development (ABCD) is a large longitudinal dataset in the US that gathered multiple measures on biology, psychology, and social context in children across 21 research sites.

“The Adolescent Brain and Cognitive Development (ABCD) is a large longitudinal dataset in the US that gathered multiple measures on biology, psychology, and social context in children across 21 research sites.”

• cites [78] Volkow, N. D. et al. The conception of the ABCD study: From substance use to a broad NIH collaboration. Dev. Cogn. Neurosci. 32, 4–7 (2018). sourceNot checked yet

  Not checked in the free run.