Young Adolescents’ Digital Technology Use and Mental Health Symptoms: Little Evidence of Longitudinal or Daily Linkages

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

Adolescents sent about 47 text messages per day on average, and 10.73% sent no texts during the study.

“Adolescents sent an average of 47 texts per day; 10.73% of adolescents reported never sending any texts over the study period.”

May combine two claims.

Of 24 daily quadratic effects tested, five were significant at p < .05 and three met the FDR-corrected threshold.

“Of the 24 possible daily quadratic effects tested in Q3a which examined associations between 6 daily indicators of technology use and 4 daily mental health dimensions, only five were significant at p<.05, and three of these met the FDR-corrected significance level”

May combine two claims.

Adolescents from economically disadvantaged households reported more daily screen time (5.24 hours) than those not disadvantaged (3.68 hours), a gap present for school work, communication, and content creation but not entertainment.

“Adolescents from economically disadvantaged households reported more daily screen time (5.24 hours) than adolescents from non-disadvantaged households (3.68 hours); this difference was also evident in time spent on technology for school work, communication, and creating content, but not for entertainment.”

May combine two claims.

Older age was linked to more total daily screen time, including school-work and communication technology, but not entertainment or content-creation technology.

“Age was associated with more total daily screen time, including time spent daily using technology for school work and communication but not time spent on technology for entertainment or creating content.”

May combine two claims.

In the sensitivity analysis, three new associations reached FDR-corrected significance: average school-work technology with more conduct problems (b = .28, OR = 1.32), school-work technology with more depression (b = 1.66, β = .16), and daily entertainment technology with less same-day worry (b = −.43, β = −.07).

“with 3 new associations meeting FDR-corrected significance cutoffs: average daily technology for school work was associated with higher average levels of conduct problems (b=.28, SE=.09, OR=1.32, p=<.01) and depression (b=1.66, SE=.51 β=.16, p=<.01 ) and daily time spent on technology for entertainment was associated with lower levels of same-day worry (b=−.43, SE=.14 β=−.07, p=<.01 ).”

• AssumptionRests on an assumption: The EMA-adapted depression and worry items validly measure internalizing symptoms.

May combine two claims.

African-American adolescents reported the most total daily screen time (6.16 hours) and white adolescents the least (3.51 hours), with Hispanic and other-race adolescents in between.

“African-American adolescents reported spending significantly more time per day on total screen time (6.16 hours) than white adolescents, who reported the least total screen time (3.51 hours); adolescents of Hispanic and other race/ethnicity reported levels of use between these two extremes.”

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

At least one inattention/hyperactivity symptom was reported on 44% of all study days, and 17% of adolescents reported none at any point.

“Across all study days for all participants, at least one symptom of inattention/hyperactivity was endorsed on 44% of days, with 17% of the sample never endorsing any symptoms of inattention/hyperactivity.”

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

Person-mean daily worry averaged 18.32 (SD = 17.13).

“a person-mean worry score was computed by averaging daily worry symptoms across all study days (Mean=18.32, SD= 17.13).”

• AssumptionRests on an assumption: The EMA-adapted depression and worry items validly measure internalizing symptoms.

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

A conduct problem was reported on 8% of all study days, and 66% of adolescents reported none at any point (M = .08, SD = .17, α = .72).

“Across all study days for all participants, a conduct problem was endorsed on only 8% of days, with 66% of the sample never endorsing any conduct problems.”

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

Adolescents averaged 4.18 hours of daily screen time, and 3.41% reported no technology use.

“Adolescents reported an average of 4.18 hours of daily screen time (3.41% of the sample never reported using technology).”

May combine two claims.

Social media access and use frequency were associated with more later conduct problems, but not at FDR-corrected significance.

“Adolescents’ social media access and use frequency were both related to higher levels of later conduct problems, though these associations did not meet FDR-corrected significance levels accounting for multiple comparisons.”

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

Person-mean daily depressive symptoms averaged 21.34 (SD = 12.48; α = .69).

“these three symptoms were averaged across the day to yield a daily depressive symptom score, and a person-mean depression score was computed by averaging daily depressive symptoms across all study days (M=21.34, SD= 12.48; α =.69).”

• AssumptionRests on an assumption: The EMA-adapted depression and worry items validly measure internalizing symptoms.

May combine two claims.

Phone ownership, social media access, and social media use frequency were unrelated to later depression, worry, and inattention/hyperactivity symptoms.

“adolescents’ phone ownership, social media access, and frequency of social media use were unrelated to later depression, worry, and inattention/hyperactivity symptoms.”

• AssumptionRests on an assumption: The EMA-adapted depression and worry items validly measure internalizing symptoms.

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

On a typical day, the average adolescent reported 0.82 symptoms of inattention/hyperactivity (SD = 0.88; α = .83).

“The average participant endorsed .82 symptoms of inattention/hyperactivity on an average day (Mean=.82, SD=.88; α =.83).”

May combine two claims.

The sample was mostly young to mid adolescents (full range 10-17, with 94% of the T2 EMA sample aged 12-15).

“we sampled mostly young to mid adolescents (full range 10–17 with 94% of the T2 EMA sample falling between the ages of 12–15)”

May combine two claims.

Of 96 daily linear interactions tested, 10 met p ≤ .05 and all became non-significant after FDR correction.

“Of the 96 potential daily linear interactions tested in Q4a (high risk moderation of Question 2a), examining effects of 6 daily technology use variables on 4 outcomes as moderated by 4 indicators of risk (age, economic disadvantage, gender, and T1 risk), only 10 met a significance cutoff of p≤.05, and all were reduced to non-significance when FDR-corrected to account for multiple comparisons”

The quote only partly states this claim.

Daily school-work technology use had a small but significant quadratic relation with daily inattention/hyperactivity (b = −.01, SE < .01, p < .01), an inverted U in which more use meant more symptoms for most observations but fewer in the top 1%.

“Adolescents’ daily technology use for school work had a small but significant quadratic relation with daily symptoms of inattention/hyperactivity (bquadratic=−.01, SE<.01, p<.01), which took the form of an inverted “U””

May combine two claims.

For most adolescents, more average content-creation technology use went with more depressive symptoms, but for about 5% at the top of the distribution (above roughly 1.6 hours/day) the association turned negative.

“For the majority of the sample, as technology use for creating content increased, so too did depressive symptoms (individuals who reported more technology use on average also reported higher average depressive symptoms). For those individuals at the top end of the technology use spectrum, however (~5% of the sample in the light grey and white regions of the graph, falling above about 1.6 average”

• AssumptionRests on an assumption: The EMA-adapted depression and worry items validly measure internalizing symptoms.

May combine two claims.

Of 96 person-level linear interactions tested, 13 met p ≤ .05 and only 2 met the FDR-corrected threshold.

“Of the 96 potential person-level linear interactions tested in Q4b (high risk moderation of Question 2b; supplemental Tables S3–S6), 13 met a significance cutoff of p≤.05. Of those, only 2 met the FDR-corrected cutoff for significance accounting for multiple comparisons.”

May combine two claims.

Average content-creation technology use had quadratic associations with average depression (b = −2.00, SE = .47), average inattention/hyperactivity (b = −.80, SE = .17), and average conduct problems (b = −.39, SE = .13).

“average technology for creating content had similar quadratic associations with average depressive symptoms (bquadratic=−2.00, SE= .47, p<.01), average symptoms of inattention/hyperactivity(bquadratic=−.80, SE= .17, p<.01), and average conduct problems (bquadratic=−.39, SE= .13, p<.01).”

• AssumptionRests on an assumption: The EMA-adapted depression and worry items validly measure internalizing symptoms.

May combine two claims.

The associations between school technology use and conduct problems and depression met p<.05 but not FDR-corrected cutoffs, though they met FDR-corrected significance in sensitivity analyses with more power.

“(which met traditional cutoffs of p<.05 for statistical significance but not FDR-corrected cutoffs accounting for multiple comparisons, but which did meet FDR-corrected significance levels in sensitivity analyses with more statistical power)”

May combine two claims.

Of 24 person-level quadratic effects tested, four were significant at p < .05 and three reached the FDR-corrected threshold.

“Of the 24 possible person-level quadratic effects tested in Q3b, only four were significant at p<.05 (see Supplemental Table S1 for full results), with three reaching the FDR-corrected significance cutoff.”

May combine two claims.

The three daily quadratic associations varied in sign and magnitude and appeared driven by observations in the farthest <1% of the distribution.

“the three quadratic associations which emerged were of varying signs and magnitudes and seemed to be driven by daily observations in the very farthest reaches (<1%) of the distribution”

May combine two claims.

Daily content-creation technology use had a significant quadratic relation with daily depression (b = .08, SE = .02, p < .01), a shallow U in which the association was weakly negative for 99% of observations and positive only in the top 1%.

“Adolescents’ daily technology use for creating content had a significant quadratic relation with daily depression (bquadratic=.08, SE= .02, p<.01).”

• Also stated in: § Exploratory Study Questions · plain

  “For the vast majority of observations—99% of daily observations fall within the grey shaded region— the association between technology use for creating content and daily depressive symptoms was weakly negative”

• AssumptionRests on an assumption: The EMA-adapted depression and worry items validly measure internalizing symptoms.

May combine two claims.

The daily and person-level quadratic associations were largely inconsistent; for example, the content-creation/depression links differed in sign across levels, while the content-creation/conduct-problem links shared sign but differed greatly in magnitude.

“Notably, there was little consistency between the daily and person-level quadratic associations observed. For instance, the daily and person-level associations between depicted in Figure 2 (between the time adolescents spend on technology creating content and depressive symptoms) differ in sign, whereas those in Figure S1 (between the time adolescents spend on technology creating content and”

• AssumptionRests on an assumption: The EMA-adapted depression and worry items validly measure internalizing symptoms.

May combine two claims.

The person-level link between entertainment technology use and conduct problems was also moderated by T1 mental health risk: the T1-risk group had more conduct problems on average but no significant link to entertainment use (b = −.08, OR = .92, p = .68), while those not at T1 risk showed a positive link (b = .21, OR = 1.23, p = .04).

“The person-level association between technology use for entertainment and conduct problems was also moderated by T1 mental health risk (those in the upper quartile for T1 conduct problems were classified as being at highest risk). Although those adolescents in the T1 risk group were more likely to endorse conduct problems on average, they did not see a significant association between their average”

May combine two claims.

Daily content-creation technology use had a significant quadratic relation with daily conduct problems (b = −.11, SE = .03, p < .01), with no association for nearly all observations and lower odds of conduct problems only in the extreme tail.

“Adolescents’ daily technology use for creating content also had a significant quadratic relation with daily conduct problem symptoms (bquadratic=−.11, SE=.03, p<.01), which took the form of a downward curving line”

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

The EMA subsample had more White and fewer economically disadvantaged adolescents than the state public-school population.

“though the 395 adolescents who agreed to participate in the EMA were more likely to be White (60.6% versus 51.3%) and less likely to be economically disadvantaged (measured as current receipt of free/reduced lunch; 40.8% versus 55.4%) compared to the overall state public school population.”

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

A sensitivity analysis using FIML for daily observations with partial missingness was run; the inattention/hyperactivity models were not identified, and for the other three outcomes results matched the listwise-deletion results for 15 of 18 coefficients.

“We conducted a sensitivity analysis to determine if our decision to use listwise deletion to handle missing data at the daily level influenced study results. Q2 models were estimated using FIML for daily observations with partial missingness. Inattention/hyperactivity models were not identified and thus results are not available. For the remaining 3 outcomes, results were consistent with those us”

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

All 48 potential quadratic effects were reported to avoid selective reporting.

“Exploring potential non-linear associations involved testing 48 potential quadratic effects, all of which are reported in the interest of full disclosure and to avoid selective reporting.”

Classified as probably not a substantial claim.

Few studies have examined within-person daily questions using EMA.

“Who questions are largely seen in the literature’s prevalent cross-sectional designs, but few studies have tackled when study questions using EMA designs.”

Classified as probably not a substantial claim.

Psychological distress was measured with six items from the well-validated Kessler K6 scale.

“Psychological distress was assessed with six items from the well-validated Kessler (K6) Psychological Distress (Furukawa, Kessler, Slade, & Andrews, 2003; Green, Gruber, Sampson, Zaslavsky, & Kessler, 2010).”

• cites Furukawa, Kessler, Slade, & Andrews, 2003Not checked yet

  Not checked in the free run.

• cites Green, Gruber, Sampson, Zaslavsky, & Kessler, 2010Not checked yet

  Not checked in the free run.

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

Analyses used Mplus 7.2 with FIML to handle person-level missing data and MLR to handle non-normality, and listwise deletion at the daily level.

“All analyses were conducted in Mplus 7.2 (Muthén & Muthén, 2017) with FIML estimation to handle missing data at the person level (level 2) and MLR estimation to account for non-normality. Listwise deletion was employed at the daily level (level 1)”

• cites Muthén & Muthén, 2017Not checked yet

  Not checked in the free run.

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

The Early Adolescent Temperament Questionnaire is a widely used measure of effortful control.

“Adolescents responded to 16 questions from the Early Adolescent Temperament Questionnaire (L. K. Ellis & Rothbart, 2001), a widely used instrument to measure effortful control, deficits in which are associated with risk for ADHD (Eisenberg et al., 2009; Martel & Nigg, 2006).”

• cites L. K. Ellis & Rothbart, 2001Not checked yet

  Not checked in the free run.

• cites Eisenberg et al., 2009Not checked yet

  Not checked in the free run.

• cites Martel & Nigg, 2006Not checked yet

  Not checked in the free run.

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

Eighty percent of EMA prompts were answered, yielding 13,017 observations across 5270 study days.

“Eighty percent of survey prompts were answered, resulting in 13,017 total observations over 5270 study days for the measures analyzed here.”

Classified as probably not a substantial claim.

The Benjamini-Hochberg procedure was applied to control the false discovery rate at .05 across the many tests.

“the Benjamini Hochberg procedure for adjusted significance tests was utilized to manage the False Discovery Rate (FDR; Benjamini and Hochberg, 1995)”

• cites Benjamini and Hochberg, 1995Not checked yet

  Not checked in the free run.

Classified as probably not a substantial claim.

The EMA design allows each adolescent to act as his or her own control over time, holding stable characteristics constant when testing within-person daily associations.

“EMA is unique in that the daily associations allow for the adolescent to serve as his or her own control across time, testing whether changes in adolescents’ technology use are associated with within-individual risk for same-day mental health symptoms, holding all stable characteristics (e.g., sex, race/ethnicity, or socioeconomic status) constant over time.”

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

The false-discovery-rate approach is less conservative than alternatives such as Bonferroni correction.

“The FDR method of error control is less conservative than other alternatives like the Bonferroni method (McDonald, 2015)”

• cites McDonald, 2015Not checked yet

  Not checked in the free run.

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

The Mplus output files, including syntax and variance/covariance matrices, are publicly posted at adaptlab.org so the analyses can be replicated.

“all Mplus output files (including syntax and variance/covariance matrices which allows for replication) are available at the adaptlab.org website.”

May combine two claims.

Associations between and within adolescents differed in both magnitude and sign, illustrating the ecological fallacy.

“Nonetheless, these results, in which associations between and within adolescents differ not only in their magnitude, but also in their signs, serve as a reminder of an old and cardinal rule of interpreting aggregate (between-person) associations and attempting to generalize them to the individual – the ecological fallacy (Molenaar, 2004).”

• cites Molenaar, 2004Not checked yet

  Not checked in the free run.

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

All 192 potential daily and person-level interactions were reported to avoid selective reporting.

“We report all 192 potential daily and person-level interactions tested in the interest of full disclosure and to avoid selective reporting.”

May combine two claims.

The three daily quadratic relations should be read with extreme caution and may not be trustworthy, since they are driven by tail observations and a tiny minority of days.

“Notably, these three observed quadratic relations should be interpreted with extreme caution and may not be trustworthy, given that these curvilinear associations are driven by observations in the tail of the distributions and are based on a tiny minority of daily observations,.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

Many design decisions can shape final study results.

“We recognize that there are many design decisions that can shape final study results (Orben & Przybylski, 2019)”

• cites Orben & Przybylski, 2019Not checked yet

  Not checked in the free run.

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

One study found that the amount of Facebook use was associated with worsened affect in adults.

“but in contrast to one study which showed associations between quantity of Facebook use and worsened affect among adults (Kross et al., 2013)”

• cites Kross et al., 2013Not checked yet

  Not checked in the free run.

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

The study was not formally preregistered.

“we did not formally preregister the present study”

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

The person-level quadratic relations should be read with caution because they rest on a small subset of individuals.

“Again, we urge caution in interpreting these quadratic relations, as it they are based on a small subset of individuals in the sample.”

May combine two claims.

This age range may mask unique processes in early adolescence and does not allow conclusions about older adolescents.

“which may mask unique processes in early adolescence (for example) and does not allow us to speak to associations among older adolescents”

Classified as probably not a substantial claim.

The study's conclusions are strengthened by in-the-moment EMA assessment of multiple technology use and mental health domains.

“utilization of in-the-moment EMA assessment of multiple domains of technology use and mental health”

Classified as probably not a substantial claim.

The study's conclusions are strengthened by including daily and person-level control variables to account for confounding.

“the inclusion of daily and person-level control variables to account for potential confounding”

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

It is difficult to determine whether this screen-time difference is due to data collection method, younger sample age, or real differences between the North Carolina representative sample and the nationally representative adolescents surveyed.

“It is difficult to know if this difference is attributable to a difference in data collection method (daily reporting or a single survey), the younger age of our sample, or real differences in levels of technology usage between our North Carolina population representative sample and the nationally representative adolescents surveyed in this study.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

The study's conclusions are strengthened by recent data collection.

“Our study’s conclusions are strengthened by recent data collection”

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

The study's conclusions are strengthened by transparency in presenting null findings, which revealed few significant associations across hundreds of planned and exploratory models.

“and our transparency around the presentation of null findings, which revealed few significant associations across 100s of planned and exploratory models”

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

The study transparently presented all results from dozens of estimated models and made study materials available for replication.

“we have transparently presented all the results of the dozens of models estimated and have made available study materials to facilitate replication”

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

Recent research suggests that non-linear associations between technology and mental health may partly explain the lack of clarity, with extreme users possibly experiencing small mental health decrements.

“Recent research has suggested that the lack of clarity in the literature may be due at least in part to the presence of non-linear associations between technology and mental health, such that ill effects are not seen for most use, but that extreme users may see small decrements in mental health (Przybylski & Weinstein, 2017).”

• cites Przybylski & Weinstein, 2017Not checked yet

  Not checked in the free run.

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

Technology usage and mental health symptoms were captured daily using a method known to reduce reporting bias.

“although technology usage and mental health symptoms were captured daily and through a method that is known to reduce bias in reporting”

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

Daily technology use measures help understand the purpose of time spent and are not influenced by shifting platform fads.

“Although our measures of daily technology use facilitate understanding the purpose of time spent on technology (and are thus not influenced by shifting fads in platforms)”

May combine two claims.

The study's conclusions are strengthened by a sample diverse on multiple risk indicators and drawn from a population representative sampling frame of public school students.

“our sample (diverse on multiple indicators of risk) drawn from a population representative sampling frame of those enrolled in public schools”

Classified as probably not a substantial claim.

Using few items was necessary to reduce participant burden from multiple daily surveys.

“(which was necessary to reduce participant burden in completing multiple surveys each day)”

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

Much parent concern and policy focus on smartphone use centers on early adolescence.

“This focus on early to mid-adolescence is important, given that much of parent concern and policy-relevant focus on smartphone use focuses on early adolescence (e.g. the Wait until 8th initiative; Thayer, 2017).”

• cites Thayer, 2017Not checked yet

  Not checked in the free run.

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

The study used internal procedures to pre-specify primary study questions and hypotheses.

“although we did use internal procedures for pre-specifying our primary study questions and hypotheses”

May combine two claims.

This content-creation pattern is opposite to the usual goldilocks hypothesis, with the heaviest users possibly having better mental health.

“Interestingly, this pattern is in the opposite direction of that usually put forth about the goldilocks hypothesis (that the highest users will be at the greatest risk). Here, instead, we see that those adolescents who spend the most time on technology creating their own content may instead be enjoying better mental health.”