Objective

pmc

PLoS One

440

plosone

101285081

plos

PLOS One

1932-6203

PLOS

PMC13138621

PMC13138621.1

13138621

42081499

10.1371/journal.pone.0347582

PONE-D-25-47753

Research Article

Medicine and Health Sciences

Medical Conditions

Neurodevelopmental Disorders

Adhd

Biology and Life Sciences

Neuroscience

Developmental Neuroscience

Neurodevelopmental Disorders

Adhd

Medicine and Health Sciences

Neurology

Neurodevelopmental Disorders

Adhd

Medicine and Health Sciences

Mental Health and Psychiatry

Neuropsychiatric Disorders

Adhd

Social Sciences

Sociology

Education

Schools

Biology and Life Sciences

Neuroscience

Cognitive Science

Cognitive Psychology

Learning

Human Learning

Biology and Life Sciences

Psychology

Cognitive Psychology

Learning

Human Learning

Social Sciences

Psychology

Cognitive Psychology

Learning

Human Learning

Biology and Life Sciences

Neuroscience

Learning and Memory

Learning

Human Learning

Medicine and Health Sciences

Epidemiology

Medical Risk Factors

Physical Sciences

Mathematics

Probability Theory

Random Variables

Covariance

People and Places

Population Groupings

Age Groups

Children

People and Places

Population Groupings

Families

Children

Medicine and Health Sciences

Mental Health and Psychiatry

Mood Disorders

Depression

Research and Analysis Methods

Research Design

Longitudinal Studies

The longitudinal relation between adolescents’ learning outcomes and internalizing symptoms: The role of ADHD symptoms

Learning outcomes, internalising and ADHD symptoms

https://orcid.org/0000-0002-4091-7054

Visser

Linda

ConceptualizationFormal analysisWriting – original draft ¹ ² ³ Ehm

Jan-Henning

ValidationVisualizationWriting – review & editing ³ ⁴

https://orcid.org/0000-0003-0875-8249

Brandenburg

Janin

ConceptualizationValidationWriting – review & editing ⁵ *

Department of Developmental Psychopathology, Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands

DIPF | Leibniz Institute for Research and Information in Education, Frankfurt am Main, Germany

Center for Research on Individual Development and Adaptive Education of Children at Risk (IDeA), Frankfurt am Main, Germany

Institute for Psychology, Heidelberg University of Education, Heidelberg, Germany

Department of Rehabilitation Sciences, TU Dortmund University, Germany

Chen

Mu-Hong

Editor

Taipei Veterans General Hospital, TAIWAN

* E-mail: janin.brandenburg@tu-dortmund.de

Competing Interests: The authors have declared that no competing interests exist.

452026

2026

215

512858

e0347582

292025542026

040520260505202612052026

2026

Visser et al

https://creativecommons.org/licenses/by/4.0/

The longitudinal relation between adolescents' learning outcomes and internalizing symptoms: The role of ADHD symptoms.

215452026e0347582e0347582PLoS One10.1371/journal.pone.0347582

42081499

The longitudinal relation between adolescents' learning outcomes and internalizing symptoms: The role of ADHD symptoms.

215452026e0347582e0347582PLoS One10.1371/journal.pone.0347582

42081499

Objective

The literature on bivariate relations between learning outcomes and symptoms of anxiety, depression, and ADHD is extensive. Much less research has been done into the trivariate longitudinal relations between learning outcomes, internalizing symptoms, and ADHD symptoms, which was the focus of the current study.

Method

The sample from the Adolescent Brain Cognitive Development (ABCD-) Study was largely representative for the US population in terms of, i.e., race and ethnicity and included 11,867 children aged 9 or 10 years (47.8% female; 52.2% male) at the start.

Results

The results of bi- and trivariate latent change score modelling with four timepoints showed that school records and symptoms of anxiety or depression were related, but school records did not predict these symptoms at the next timepoint, nor the other way around. ADHD was associated with both school records and symptoms of anxiety/ depression. Depression symptoms were a negative leading indicator of subsequent changes in ADHD.

Conclusions

The results suggest that ADHD symptoms do not form the main explanatory factor for the relation between school records and internalizing symptoms. They imply that it is important to recognize signs of depression at an early stage, so that such secondary problems can be prevented. Future research is needed to find underlying risk factors that can explain comorbidity, which can help identifying children with increased risk at an early stage.

The author(s) received no specific funding for this work.

pmc-status-qastatus

pmc-status-live

yes

pmc-status-embargo

pmc-status-released

yes

pmc-prop-open-access

yes

pmc-prop-olf

pmc-prop-manuscript

pmc-prop-legally-suppressed

pmc-prop-has-pdf

yes

pmc-prop-has-supplement

yes

pmc-prop-pdf-only

pmc-prop-suppress-copyright

pmc-prop-is-real-version

pmc-prop-is-scanned-article

pmc-prop-preprint

pmc-prop-in-epmc

yes

pmc-license-ref

CC BY

Data Availability

The data underlying the results presented in the study stem from the ABCD study and are available from the NIH Brain Development Cohorts (NBDC) Data Sharing Platform (https://www.nbdc-datahub.org/abcd-study). The preregistration for this research are available at https://osf.io/yw5jv/overview?view_only=c258b240352a4d4fa3b309a79d656ce9. The analytic code necessary to reproduce the analyses is available at https://osf.io/gw8d3/overview?view_only=5bff385bc7074df8b9bd0bb75bf1e5f2.

Data Availability

Introduction

If children have persistent difficulties in reading, spelling, and/or mathematics despite appropriate schooling and in absence of other disorders that could explain these difficulties, the diagnosis of a specific learning disorder (SLD) [1] applies. In the literature about SLD, the presence of comorbidities with different types of behavioral problems is well established. The estimates on prevalence rates for these comorbidities vary widely depending on the criteria used. In general, symptoms of attention deficit/ hyperactivity disorder (ADHD) seem to be the most common comorbidity for SLD [e.g., 2,3].

SLD is related to higher rates of internalizing behavior problems as well, including symptoms of both anxiety and depression [4,5]. Especially in cases in which an SLD is present in multiple learning domains, the risk for multiple forms of behavioral problems is high [5]. This yields the question if the comorbidity between SLD and internalizing problems could be explained by co-occurring symptoms of ADHD, which indeed seems to be the case for both anxiety and depression symptoms, at least partly [6]. However, the risk for developing internalizing problems still appears higher in cases of comorbid SLD and ADHD, compared to ADHD alone [7].

In the context of the multiple deficit model, neurodevelopmental disorders are believed to arise due to the probabilistic contribution of various risk factors. Comorbidity between different disorders can be explained by common risk factors [8]. To increase our understanding of neurodevelopmental disorders and comorbidity based on the multiple deficit model, longitudinal studies are needed to evaluate the relation between various symptoms and risk factors over time.

Therefore, the current study focused on the longitudinal relation between learning outcomes, internalizing symptoms, and ADHD symptoms in a general sample of early adolescents. Increased knowledge about this relation in the general sample can help increase our understanding of comorbidity on the clinical level. Indeed, ADHD- and internalising symptoms appear to relate not only to SLD on a clinical level, but also to learning outcomes in general [9,10]. In addition, not only clinical, but also subclinical symptoms have been associated with reduced quality of life in children [11]. Therefore, we took both symptom levels into account by focusing on the continuum of symptoms and school outcomes, rather than on neurodevelopmental disorders as categories. We focused on early adolescence, as this developmental period is known to be a critical period for internalizing problems to arise [12].

Longitudinal trajectories of learning outcomes and internalizing symptoms

Before studying the longitudinal relation, an understanding is needed about the longitudinal trajectories of the individual constructs. With respect to learning outcomes, longitudinal studies in the United States have shown slight declines, at least from grade 6 onwards [13–15], which might be related to the transition to middle school and was found irrespective of gender [15]. With respect to anxiety, earlier studies have shown a decrease in symptoms during early adolescence [16], followed by a subsequent increase in symptoms [e.g., 17,18], showing a discontinuous trajectory. The decrease in symptoms seems to be higher in children with higher initial symptom levels [16,18]. Important to keep in mind is that the individual variability in the development of anxiety symptoms is high [19].

With respect to the development of depression symptoms in early adolescence, inconsistent results have been found. McLaughlin and King [16] found no significant change in adolescents between 10 and 15 years of age. Cohen and colleagues [17] followed children from 7 to 15 years of age and found a slight decrease in symptoms until the age of 12, followed by a slight increase. Ormel and colleagues [18] found an increase from age 15 onwards. Based on a systematic review [20], depression symptoms seem to peek around 15–17 years of age, thus decreasing again afterwards. The trend in symptom change seems to be independent from initial symptom levels [16,21]. For both anxiety and depression, McLaughlin and King [16] found higher symptom levels in girls, but no gender differences with respect to change. It seems that symptoms of negative affect, anhedonia, and anxious arousal develop relatively independently from each other, at least in late adolescence [22].

How does the development of anxiety and depression symptoms relate to school outcomes? Levels of anxiety and achievement are negatively correlated [19], but this relation has been found to be weak (r = −.06) [23]. Certain types of anxiety might be related to school outcomes more than others. For example, Morin and colleagues [24] found that children with anxiety related to school transition showed the lowest school outcomes. For depression, based on a meta-analysis, the influence of symptoms on subsequent school outcomes is small but significant (pooled r= −0.19), independent from gender, also after adjusting for various confounding variables [25].

Results regarding the direction of effects are mixed, with some studies mainly finding an effect from achievement on later depression [26], others mainly finding an effect from depression on subsequent achievement [27], again others finding a negative relation (random intercept), but not a dynamic one [28]. Weidman and colleagues [29] did find a transactional relation, with higher levels of depression and anxiety predicting lower school outcomes as well as the other way around. In a study by Chen et al. [30] among elementary school children, the results of a cross-lagged panel model showed effects of school outcomes on depression symptoms and vice versa, but those of random-intercept cross-lagged panel models only showed an effect of depressive symptoms on school outcomes.

Explanations for the bivariate relation between internalizing symptoms and school outcomes are given by the academic incompetence hypothesis (academic incompetence leads to or worsens internalizing problems) and the adjustment erosion hypothesis (internalizing symptoms have a negative effect on school outcomes) [e.g., 28]. Based on the current literature, there is slightly more support for the adjustment erosion hypothesis, but a firm conclusion is not possible because most studies only focus on one direction of effect or have methodological shortcomings [31].

The role of ADHD-symptoms

Alternative explanations for the relation between internalizing symptoms and school outcomes are given by the transactional effects model and the shared risk hypothesis (a third variable increases the risk for problems in both domains) [e.g., 31]. Focusing on this last hypothesis, one variable that has been linked to school outcomes as well as internalizing symptoms are symptoms of ADHD. Symptoms of ADHD [32] as well as an ADHD diagnosis [e.g., 33] appear relatively stable. It is well established that ADHD symptoms influence learning outcomes [e.g., 34–36]. Less clear is if the disadvantage for affected children increases with age [35,36] and if ADHD symptoms are related to the growth in school performance [34].

Earlier studies have also shown that ADHD symptoms have a significant influence on subsequent levels of anxiety [37] as well as on changes in anxiety [38]. Vice versa, anxiety influences subsequent levels of ADHD in adolescents [37], but not in young children [39].

Where the literature on trajectories of the individual constructs and on bivariate relations is extensive, much less research has been done into the trivariate relation between learning outcomes, internalizing symptoms, and ADHD symptoms. As mentioned before, earlier correlational research found that ADHD symptoms partly explain the relation between anxiety and learning outcomes [6]. In addition, a 4-year longitudinal study among college students [40] identified depression symptoms as one of the strongest predictors for college outcomes in students with ADHD, which were lower compared to those of students without ADHD.

Research questions and hypotheses

Based on the literature described above, the current study is confirmatory in nature and based on the following three sets of preregistered [41] research questions and hypotheses.

First, we evaluated how the shape of the developmental trajectories looks like for:

school records. We expected to find a decline from grade 6 onwards, which is related to the transition to middle school [14,15].

anxiety symptoms. We expected to find a decrease in anxiety symptoms with age (negative linear slope) [e.g., 16–18] and greater declines in children with higher anxiety levels at baseline (a negative correlation between the intercept and slope) [e.g., 16,18].

depressive symptoms. We expected to find no change at first and an increase in symptoms starting around age 12 (grade 7) [17,21]. We expected this change from grade 7 onwards to be caused by a linear slope factor [21], not dependent on the previous time point [17]. Also, we did not expect differences in change depending on the initial level of symptoms [16,42].

In addition, we hypothesized for each of the variables a to c that the rank order stability between adjacent time points (1-year interval) would be high, whereas the stability between more distant time points would be lower.

Second, we evaluated the longitudinal relation between school records and:

anxiety symptoms. We expected a negative correlation between the initial levels of anxiety and achievement (significant correlation between the baseline factors) [e.g., 19,29]. In addition, we expected to find low achievement to predict higher levels of anxiety in the next school year [29].

depressive symptoms. On a theoretical basis we expected to find effects of depression on subsequent achievement and vice versa (both an academic incompetence effect and an adjustment erosion effect) [28]. In addition, we expected a negative correlation between the initial levels of school records and depression [28].

Third, we evaluated the trivariate dynamic relation between ADHD-symptoms, school records and:

anxiety symptoms. We hypothesized that:

ADHD symptoms have a significant influence on both the starting level (intercept) of school records and on changes in school records (increasing disadvantage) [35,36];

The starting levels of ADHD and anxiety are positively correlated [5]. In addition, ADHD symptoms have a significant influence on subsequent levels of anxiety and anxiety has a significant influence of subsequent levels of ADHD [37]. We also expected an influence of ADHD symptoms on the change in anxiety [38];

iii

if a relation between school records and subsequent anxiety is found under step 2, this can be explained by ADHD symptoms [6].

depression symptoms. We hypothesized that:

ADHD symptoms have a significant influence on the starting level of depression [5] as well as on the change in depression (slope) [43,44];

if relations between school records and subsequent depression symptoms and between depression and subsequent school records are found, these can be explained by ADHD symptoms [6,40].

Materials and methodsData and sample

We used the data from a large-scale longitudinal study in the United States into the biological and behavioral development of children from 9–10 years of age onwards: the Adolescent Brain Cognitive Development Study [45], funded by the National Institutes of Health (NIH). The baseline measure (T0) took place between September 2016 and August 2018 [46] with a sample of 11,867 children aged 9 or 10 years old. The current study is based on the data from data release 5.0 regarding psychopathology, neurocognition, and school records from the first four time points: T0 to T3. Because data for school records were missing for the fourth time point, we did not include any data from T4. Although data for the other variables were available, using alternative methods such as imputation was not an option, because school records formed an essential element in the analyses and were missing for all children at T4. Most of the children were 12–13 years old at T3. Table 1 shows descriptive statistics for gender, age, and grade at the four time points. We did not exclude any cases.

10.1371/journal.pone.0347582.t001

Table 1Descriptive Statistics of the ABCD Sample at Baseline (T0), T1, T2, and T3.

T	N (%)	Age in years;months			Gender n (%)			Grade n (%)
T	N (%)	M	SD	Range	female	male	other	3	4	5	6	7	8	9
0	11,867 (100%)	9;11	0;8	8;11–11;1	5,676 (47.8%)	6,190 (52.2%)	1(0.0%)	2,032 (17.1%)	5,415 (45.6%)	3,984 (33.6%)	358 (3.0%)
1	11,219 (94.5%)	10;11	0;8	9;8–12;5	5,341 (47.6%)	5,863 (52.2%)	15 (0.1%)		1,958 (17.5%)	4,963 (44.2%)	3,796 (33.8%)	399 (3.6%)
2	10,972 (92.4%)	12;0	0;8	10;7–14;0	5,180 (47.2%)	5,722 (52.2%)	70 (0.6%)			1,469 (13.4%)	4,820 (43.9%)	3,856 (35.1%)	671 (6.1%)
3	10,335 (87.1%)	12;11	0;8	11;5–14;9	4,788 (46.3%)	5,319 (51.5%)	228 (2.2%)				2,009 (19.4%)	4,544 (44.0%)	3,123 (30.2%)	345 (3.3%)

Note. T = time point; In the numbers regarding Grade, only those > 100 were included.

The 21 research sites that collected data for the ABCD study obtained ethical permission via the central Institutional Review Board (cIRB) at the University of California, San Diego or via a local IRB [47]. Informed consent was obtained from both parents and children. The sample was recruited with the aim to obtain representativity for the US population with respect to age, gender, race and ethnicity, socio-economic status, and urbanicity. This aim was largely met, although children from rural areas were slightly underrepresented. For more information about the recruitment and sample, we refer to Garavan et al. [46].

Instruments

Learning outcomes were operationalized as the answer of the parent to the question “Please look at the list and pick the line that best describes your child’s grades in school last year”. The answer options were given in various ways to specify grades, in numbers, letters, as well as descriptions. For the analysis, we used the numbers: 1 = Excellent (A), 2 = Good (B), 3 = Average (C), 4 = Below Average (D), and 5 = Struggling a lot (F). If parents chose the answer option ungraded or not applicable, we recoded this answer into a missing value.

Symptoms of depression, anxiety, and ADHD are based on parent’s responses on the Child Behavior Checklist (CBCL) [48]. The CBCL is a parent-report questionnaire for assessing the behavior of school-aged children between 6 and 18 years of age. It consists of a total of 112 items that the parent answers on a 3-point Likert scale (not true, somewhat/ sometimes true, very true/ often true). We used the raw score on the DSM-oriented scales Depressive problems, Anxiety problems, and Attention Deficit/Hyperactivity problems, respectively. For these scales, the manual [48] reports a test-retest reliability of r= .80 −.93 and Cronbach’s alpha of.72 −.84. Higher raw scores indicate higher levels of problems.

With respect to validity, the CBCL has been shown to discriminate well between referred and non-referred children and correlate moderately to strongly with corresponding scales of the Behavior Assessment System for Children (BASC; r = .52 −.77) [49] and the Conners Parent Rating Scale Revised (CPRS-R; r = .71) [50]. We standardized the scores based on the data at baseline to enhance comparability and interpretation.

Data analysis

Data were prepared using RStudio [51] and analyzed using Mplus, version 8.6 [52]. In a first step, we calculated descriptive statistics for all variables of interest, taking into account gender. Because the results hinted at gender differences, we included gender in the further analyses as a covariate. Gender was coded as male = 0 and female = 1 in the dataset.

Research question 1.

We examined the shape of the developmental trajectories with latent change score modelling. Specifically, we tested the following models for school records, anxiety symptoms, and depressive symptoms:

No change model (equivalent to the intercept-only model in the latent growth model [LGM] framework)

Constant change model (equivalent to the linear growth model in the LGM framework)

Linear change model (equivalent to the quadratic growth model in the LGM framework)

Dual change score model

We constrained the residual variances of the indicators to be equal across time for model parsimony, but released this constraint for single or all indicators if modification indices indicated a model misspecification concerning this matter. The expectation was that the developmental trajectories would represent a linear latent change model or a dual change model.

To answer the research questions, we examined (a) the mean and the variance of the intercept factors and the slope factors, (b) the covariances between the intercept and the slope factors, and (c) the estimates of the proportional change parameters. We used the final longitudinal models obtained at this stage as a basis for the models related to research question 2.

Research question 2.

We used two different Bivariate Latent Change Score Models (BLCSM) [53] for testing the longitudinal relation between school records on the one side and depressive and anxiety symptoms, respectively, on the other. Based on the final latent change score models of the previous stage we included coupling (across variable) effects. The models were built stepwise, from simple to complex:

No coupling model (i.e., fixing both coupling parameters to 0)

Two unidirectional coupling models where either one of the coupling parameters is estimated

Full coupling model where both coupling parameters are jointly estimated

We put equality constraints on the coupling parameters suggesting constant dynamics. These constraints could be released for a single or all indicators if modification indices indicated a model misspecification concerning this matter. To evaluate the significance of the coupling parameters, we compared the resulting models 1–3 using the Satorra-Bentler scaled chi-square difference test to examine which of the specified models fit the data best.

For the final models, we also examined the associations between the intercept factors and slope factors across constructs (i.e., how level and change in school records is associated with level and change in anxiety/depression and vice versa). In bivariate change models, the intercorrelation between all slope factors and intercept factors is usually freely estimated per default. Concerning this assumption of correlated growths and intercepts, we, in addition, tested alternative model restrictions of the final bivariate change models, in that we specified (a) the slope covariances at zero, (b) slope intercept and covariances across constructs at zero, and (c) all connections over time at zero. Again, the resulting models were compared with the Satorra-Bentler scaled chi-square difference test.

The final bivariate latent change score models obtained at this stage formed the basis for the models related to research question 3.

Research question 3.

We added ADHD symptoms to the model longitudinally in the BLCSMs obtained at stage 2, resulting in trivariate LCSMs. We followed the steps as described above for the bivariate model (Models 1–3) for the relation of ADHD with both anxiety/ depression and with school records. To answer the Research Questions 3.a.iii and 3.b.ii (e.g., if ADHD explains a possible relation between anxiety/depression and school records), we examined the following combination of effects:

Combination 1:

an effect from ADHD on school records at the next time point,

a correlation between ADHD and depression/ anxiety at the same time point, and

a nonsignificant or decreased effect from depression/ anxiety to school records at the next time point, compared to the model without ADHD;

Combination 2:

an effect from ADHD to depression/ anxiety at the next time point,

a correlation between ADHD and school records at the same time point, and

a nonsignificant or decreased effect from school records to depression/ anxiety at the next time point, compared to the model without ADHD.

Children are nested in families and families are nested within sites. For study site, the ICCs ranged between 0.00 und 0.02. When we took into account the sites as a covariate, none of the regression coefficients were statistically significant. Therefore, and because of the low ICCs, we decided that controlling for study site was not necessary and used a two-level model controlling for the family level using “TYPE = COMPLEX” in Mplus.

Statistical criteria

We used an alpha level of.05 and judged model fit using the chi-squared test. Because the chi-squared test is sensitive to large sample sizes, we additionally used the criterion chi/df ratio < 2 [54]. Regarding fit indices, we used the root mean square error of approximation (RMSEA) ≤.06 and comparative fit index (CFI) ≥.95. Model comparison was based on the adjusted chi-squared difference test using the Satorra-Bentler scaling correction [55] and the mentioned fit indices. In addition, the dual change score model was only chosen in case of a significant proportional change component.

For all models, we used the robust Maximum Likelihood (MLR) estimator in MPlus. To test the specific hypotheses formulated, we looked if the result of interest (intercept, slope, constant change, proportional change, or coupling effect) was significant.

ResultsDescriptives

Table 2 shows the descriptive statistics for the school records and levels of depression, anxiety, and ADHD symptoms for each time point by gender. For gender we used the variable indicating the sex of the child, because this was the variable with the least missing data. The three cases with missing values for sex had indicated being male as gender and data were recoded accordingly. The sample size differed slightly for each variable due to missing data. See S1 Table for the exact sample sizes.

10.1371/journal.pone.0347582.t002

Table 2<italic toggle="yes">M</italic> (<italic toggle="yes">SD</italic>) for Standardized Scores per Time Point and by Gender as well as <italic toggle="yes">t</italic>-test Results for Differences Between Females and Males.

Time point	Gender	School records	Depression	Anxiety	ADHD symptoms
0	Female	1.60 (0.74)	−0.06 (0.94)	0.00 (0.99)	−0.17 (0.89)
	Male	1.77 (0.84)	0.06 (1.05)	0.00 (1.01)	0.16 (1.07)
	Diff.	t(10,930) = 11.67**	t(11,855) = 6.33**	t(11,810) = 0.19	t(11,760) = 18.21**
	Total	1.69 (0.80)	0.00 (1.00)	0.00 (1.00)	0.00 (1.00)
1	Female	1.58 (0.75)	0.01 (1.04)	0.01 (1.00)	−0.21 (0.86)
	Male	1.78 (0.84)	0.11 (1.13)	−0.01 (1.02)	0.09 (1.04)
	Diff.	t(10,441) = 12.67**	t(11,198) = 5.13**	t(11,134) = −1.03	t(11,104) = 16.44**
	Total	1.68 (0.80)	0.06 (1.09)	0.00 (1.01)	−0.06 (0.97)
2	Female	1.59 (0.77)	0.09 (1.12)	−0.07 (0.96)	−0.27 (.82)
	Male	1.78 (0.85)	0.11 (1.10)	−0.13 (0.94)	0.03 (1.00)
	Diff.	t(10,264) = 12.14**	t(7,975.1) = 1.01	t(7,982.7) = -2.66**	t(7,993.1) = 14.79**
	Total	1.69 (0.82)	0.10 (1.11)	−0.10 (0.95)	−0.11 (0.93)
3	Female	1.59 (0.78)	0.24 (1.29)	−0.04 (.99)	−0.27 (0.81)
	Male	1.81 (0.88)	0.17 (1.16)	−0.15 (0.93)	0.03 (1.00)
	Diff.	t(9,398.4) = 13.31**	t(7,356.7) = -2.42*	t(7,476.1) = -4.74**	t(7,556.1) = 14.32**
	Total	1.71 (0.84)	0.20 (1.23)	−0.10 (0.96)	−0.11 (0.93)

Note. Diff. = Difference between females and males; * = p <.05; ** = p <.01.

Developmental trajectories

Table 3 shows the model fit for the various models evaluated with respect to the developmental trajectories (research question 1).

10.1371/journal.pone.0347582.t003

Table 3Fit Statistics for the Models for Evaluating the Developmental Trajectories.

	Model	χ2 (df)	SCF	RMSEA[90% CI]	CFI	TLI	SRMR	AIC	sa-BIC
SR(N = 11,799)	a. no growth	657.306 (14)	1.5148	.062[.058,.067]	.941	.958	.043	82,205.294	82,222.086
	b1. constant change	613.080 (12)	1.5318	.065[.061,.070]	.945	.954	.045	82,152.684	82,177.871
	b2. = b1 with random slope	97.773 (10)	1.5735	.027[.023,.032]	.992	.992	.026	81,371.435	81,405.018
	c1. linear change	87.629 (8)	1.6968	.029[.024,.035]	.993	.991	.026	81,370.284	81,412.263
	d1. dual change score	90.422 (9)	1.5302	.028[.023,.033]	.993	.992	.026	81,357.957	81,395.738
	d2. = d1. with free autoproportions	87.993 (7)	1.5168	.031[.026,.037]	.993	.989	.026	81,357.057	81,403.234
Anxiety(N = 11,866)	a. no growth	394.299 (14)	1.782	.048[.044,.052]	.962	.973	.041	92,388.627	92,405.442
	b1. constant change	274.641 (12)	1.8636	.043[.039,.047]	.974	.978	.034	92,201.793	92,227.014
	b2. = b1 with random slope	84.183 (10)	1.8006	.025[.020,.030]	.993	.993	.023	91,845.553	91,879.181
	c1. linear change	75.070 (8)	1.9836	.027[.021,.032]	.993	.992	.023	91,846.877	91,888.912
	c2. c1 with random slope	74.417 (7)	1.8930	.028[.023,.035]	.993	.990	.022	91,840.838	91,887.077
	d1. dual change score	87.210 (9)	1.7373	.027[.022,.032]	.992	.991	.022	91,847.481	91,885.313
	d2. = d1. with free autoproportions	59.223 (7)	1.6371	.025[.019,.031]	.995	.993	.013	91,796.920	91,843.159
Depres-sion(N = 11,866)	a. no growth	734.796 (14)	2.4894	.066[.062,.070]	.889	.921	.085	103,709.951	103,726.765
	b1. constant change	500.069 (12)	2.6767	.059[.054,.063]	.925	.937	.078	103,223.249	103,248.470
	b2. = b1 with random slope	148.810 (10)	2.5869	.034[.029,.039]	.979	.979	.033	102,273.686	102,307.314
	c1. linear change	123.064 (8)	2.9504	.035[.030,.040]	.982	.978	.032	102,255.814	102,297.849
	c2. c1 with random slope	77.285 (7)	2.8461	.029[.023,.035]	.989	.985	.032	102,114.687	102,160.926
	d1. dual change score	98.560 (9)	2.3558	.029[.024,.034]	.986	.985	.033	102,122.918	102,160.750
	d2. = d1. with free autoproportions	37.811 (7)	1.8828	.019[.014,.025]	.995	.993	.011	101,965.916	102,012.155
	d3. = d1. with autoproportion for first component free	32.006 (8)	2.2307	.016[.010,.022]	.996	.995	.012	101,964.121	102,006.156
ADHD(N = 11,866)	a. no growth	507.394 (14)	1.5164	.054[.050,.059]	.968	.977	.039	82,321.230	82,338.045
	b1. constant change	365.684 (12)	1.5540	.050[.046,.054]	.977	.981	.033	82,124.070	82,149.292
	b2. = b1 with random slope	61.520 (10)	1.5685	.021[.016,.026]	.997	.997	.011	81,656.301	81,689.930
	c1. linear change	37.628 (8)	1.6910	.018[.012,.024]	.998	.998	.009	81,627.436	81,669.471
	d1. dual change score	52.787 (9)	1.4992	.020[.015,.026]	.997	.997	.012	81,640.943	81,678.775
	d2. = d1. with free autoproportions	39.330 (7)	1.5232	.020[.014,.026]	.998	.997	.008	81,625.710	81,671.949
	d3. = d1. with autoproportion for first component free	39.970 (8)	1.5050	.018[.013,.024]	.998	.997	.008	81,623.962	81,665.998

Note. SR = School Records; SCF = Scaling Factor; RMSEA = Root Mean Square Error of Approximation; CFI = Comparative Fit index; TLI = Tucker-Lewis Index; SRMR = Root Mean Square Residual; AIC = Akaike Information Criterion; sa-BIC = sample size-adjusted Bayesian Information Criterion; The ratio chi/df is > 2 in all models.

School records.

As shown in Table 3, the RMSEA had an optimal value for the constant change model with random slope but was almost identical for the dual change score model. The Satorra-Bentler chi-squared (SB-χ²) test showed the dual change score model (d1, see Table 3) to be the optimal model. More specifically, it fitted better compared to the no growth model (SB-χ² = 576.51; df = 5; p < .01) as well as the constant change models with fixed (SB-χ² = 521.12; df = 3; p < .01) or random slope (SB-χ² = 7.89; df = 1; p < .01). The linear change model did not fit better than the constant change model with random slope (SB-χ² = 4.77; df = 2; p = .092), which had a better fit than the one with fixed slope (SB-χ² = 593.42; df = 2; p < .01).

The model results of the dual change score model (d1) showed an intercept of 1.771 (p< .01; variance 0.446, p < .01) and a negative constant change component (−0.299, p = .016; variance 0.043, p < .01). Lower values for school records reflect better achievement, which means that this negative value reflects an improvement in grades over the years. The estimate for the proportional change component, reflecting the change in school records between time points based on the level of the previous time point, was 0.180 (p = .010). The combination of the constant change and proportional change components results in a relatively stable trajectory of school records across the years, with a slight increase (declining performance between time points of 0.015 on average). The intercept was negatively related to the constant change component (σ = −0.104, p < .01). The constant change component was not related to gender, but females had better grades at baseline compared to males (β = −0.171, p < .01). Fig 1 visualizes the developmental trajectories for males and females separately.

10.1371/journal.pone.0347582.g001

Fig 1Model-implied longitudinal trajectories for females (dashed lines) and males (solid lines) across four measurementpoints (T0–T3).

(A) School grades (raw scores); (B) Anxiety symptoms (z-values); (C) Depression symptoms (z-values); (D) ADHD symptoms (z-values).

Anxiety symptoms.

For symptoms of anxiety, the constant change model with random slopes (model b2) appeared to fit the data best. Although the fit criteria were very comparable between the constant change, linear change, and dual change score models (see Table 3), the SB-χ² test showed that the constant change model with random slope fitted better compared to the one with fixed slope (SB-χ² = 165.35; df = 2; p < .01), which, in turn, fitted better than the no growth model (SB-χ² = 147.65; df = 2; p < .01). The linear change model nor the dual change score model fitted better than the constant change model with random slopes (SB-χ² = 3.71; df = 2; p = .156 and SB-χ² = 0.03; df = 1; p = .864, respectively). In addition, the proportionality components in the dual change score model were not significant (β = 0.02; p = .863), which speaks against this model.

The result of the constant change model with random slopes (model b2) showed that the intercept of 0.015 was not significantly different from zero (p = .261) but had a variance of 0.713 (p < .01), showing that students differed in their predicted scores at baseline. The slope was −0.054 (p < .01), reflecting an average decrease in anxiety between time points, which differed between students (residual variance = 0.027; p < .01). The variability in scores at the different time points not accounted for by the model was 0.314 (p < .01). There was a significant covariance between the intercept and slope (−0.048; p < .01). The anxiety scores at baseline did not differ by gender (p = .556), but females showed slightly less decrease in anxiety between time points than males (β = 0.040; p < .01).

Depression symptoms.

The developmental trajectory for symptoms of depression appeared to be reflected best by a dual change score model in which the first proportional change component was freely estimated and the other two were set to be equal (see model d3 in Table 3). This was reflected not only in the fit criteria, but also in the results of the SB-χ² tests: A dual change score model fitted better than the no change model (SB-χ² = 585.01; df = 5; p < .01) and the constant change model with fixed slope (SB-χ² = 303.99; df = 3; p < .01) or random slope (SB-χ² = 32.74; df = 1; p < .01). Further, the dual change score model fitted better when the proportional change components were freely estimated (SB-χ² = 40.14; df = 2; p < .01) and even better when only the first component was freely estimated (SB-χ² = 0.04; df = 1; p = .834).

In the resulting model d3, the estimated depression score at baseline was 0.069 (p < .01) with significant between child variability (variance = 0.598; p < .01). The negative constant change component indicated a slight general decrease in depression levels over time (µ = −0.056; p = .011; residual variance = 0.680; p = .008), whereas the positive proportional change components of 1.194 (p < .01; T0 to T1) and 0.882 (p < .01; T1 to T2 and T2 to T3) suggested that individuals with higher symptom levels were at increased risk of symptom maintenance. Overall, this resulted in a progressive increase in symptoms over time. There was a significant negative covariance between the intercept and constant change component (σ = –.631, p < .01). With respect to gender, females showed lower levels of depression at baseline (β = −0.115; p < .01) and a less negative change (β = 0.153; p < .01). The combination of a negative constant change with positive proportional change components, taking into account the gender effects, hints at a progressive increase in symptoms over time for females and only slight increase for males, see Fig 1.

ADHD.

Although not part of the first research question, the developmental trajectory for ADHD was evaluated as well, as a basis for the trivariate model related to research question 3. The results are comparable to those of depression: The optimal model was a dual change score model in which the first proportional change component was freely estimated and the other two were set to be equal. A dual change score model fitted better than the no change model (SB-χ² = 446.10; df = 5; p < .01) and the constant change model with fixed slope (SB-χ₂ = 284.65; df = 3; p < .01) or random slope (SB-χ² = 7.92; df = 1; p < .01). A dual change score model fitted better when the proportional change components were freely estimated (SB-χ² = 13,59; df = 2; p < .01), but this freely estimated model did not fit better than one in which only the first component was freely estimated (SB-χ² = 0.18; df = 1; p = .672).

In the resulting model d3, the estimated ADHD score at baseline was 0.151 (p < .01) with significant between child variability (variance = 0.764; p < .01). The constant change component was not significant (µ = 0.014; p = .067; residual variance = 0.057; p = .005). The effect of ADHD symptoms at T0 on the subsequent change in symptoms was estimated at −0.243 (p < .01) and the subsequent two proportional change components at −0.284 (p < .01). This resulted in an average trajectory of decreasing symptom levels over time. Higher ADHD scores at baseline were associated with greater increases over time (σ = .142, p < .01). However, when considered together with the negative proportional change components, this effect suggests that children with initially high ADHD symptoms showed less escalation and tend toward stabilization, whereas increases are more pronounced among those starting with low symptom levels. With respect to gender, the results showed that females had clearly lower levels of ADHD at baseline (β = −0.320; p < .01) compared to males and a smaller decrease between time points (β = −0.072; p < .01).

Relation between school records and anxiety/ depression symptoms

Table 4 shows the model fit for the various bivariate (research question 2) and trivariate (research question 3) latent change score models evaluated.

10.1371/journal.pone.0347582.t004

Table 4Fit Statistics for the Bivariate and Trivariate Latent Change Score Models.

	Model	χ2 (df)	SCF	RMSEA[90% CI]	CFI	TLI	SRMR	AIC	sa-BIC
Anxiety & SR(N = 11,867)	a. no coupling	199.806 (30)	1.5057	.022[.019,.025]	.993	.992	.021	172,871.795	172,964.275
	b. unidirectional(school records → Δ anxiety)	197.891 (29)	1.5043	.022[.019,.025]	.993	.991	.021	172,870.628	172,967.311
	c. unidirectional(anxiety → Δ school records)	198.536 (29)	1.5085	.022[.019,.025]	.993	.991	.021	172,872.435	172,969.119
	d. full coupling	196.518 (28)	1.5023	.023[.020,.026]	.993	.991	.021	172,870.176	172,971.063
	a1. = a. with slope covariance @0	205.947 (31)	1.4956	.022[.019,.025]	.993	.992	.021	172,876.950	172,965.226
	a2. = a. with covariance anxiety intercept - SR slope @0	204.043 (31)	1.5120	.022[.019,.025]	.993	.992	.021	172,877.437	172,965.714
	a3. = a. with covariance SR intercept – anxiety slope @0	221.085 (31)	1.4963	.023[.020,.026]	.992	.991	.023	172,899.745	172,988.022
	a4. = a. with all connections @0	227.973 (33)	1.4921	.022[.020,.025]	.992	.991	.023	172,905.097	172,984.966
Depression & SR(N = 11,867)	a. no coupling	143.967 (28)	1.6518	.019[.016,.022]	.994	.993	.018	182,571.309	182,672.197
	b. unidirectional(school records → Δ depression)	141.458 (27)	1.6565	.019[.016,.022]	.994	.992	.018	182,569.824	182,674.914
	c. unidirectional(depression → Δ school records)	140.480 (27)	1.6657	.019[.016,.022]	.994	.992	.018	182,569.491	182,674.582
	d. full coupling	137.502 (26)	1.6693	.019[.016,.022]	.994	.992	.018	182,567.030	182,676.324
	a1. = a. with slope covariance @0	146.381 (29)	1.6700	.018[.016,.021]	.994	.993	.018	182,575.952	182,672.636
	a2. = a. with covariance depression intercept – SR slope @0	145.356 (29)	1.6620	.018[.015,.021]	.994	.993	.018	182,573.082	182,669.766
	a3. = a. with covariance SR intercept – depression slope @0	283.123 (29)	1.5485	.027[.024,.030]	.987	.984	.025	182,769.901	182,866.584
	a4. = a. with all connections @0	263.706 (31)	1.6633	.025[.022,.028]	.988	.986	.025	182,766.110	182,854.386
	a12. = combination of models a1. and a2.	169.082 (30)	1.6422	.020[.017,.023]	.993	.992	.019	182,607.161	182,699.641
Anxiety, SR & ADHD(N = 11,867)	a. no coupling	272.523 (60)	1.4526	.017[.015,.019]	.996	.994	.016	245,720.890	245,897.442
	b1. unidirectional(ADHD → Δ anxiety)	273.382 (59)	1.4445	.017[.015,.019]	.996	.994	.016	245,721.915	245,902.671
	b2. unidirectional(anxiety → Δ ADHD)	271.451 (59)	1.4463	.017[.015,.020]	.996	.994	.015	245,719.609	245,900.365
	b3. full coupling anxiety – ADHD	272.651 (58)	1.4376	.018[.016,.020]	.996	.994	.015	245,720.985	245,905.945
	c1. unidirectional(ADHD → Δ school records)	272.564 (59)	1.4490	.017[.015,.020]	.996	.994	.015	245,721.968	245,902.724
	c2. unidirectional(school records → Δ ADHD)	269.105 (59)	1.4465	.017[.015,.019]	.996	.994	.016	245,716.266	245,897.022
	c3. full coupling SR – ADHD	270.002 (58)	1.4410	.018[.015,.020]	.996	.994	.016	245,718.092	245,903.052
Depression, SR & ADHD(N = 11,867)	a. no coupling	258.835 (59)	1.5508	.017[.015,.019]	.995	.994	.015	254,624.065	254,804.821
	b1. unidirectional(ADHD → Δ depression)	257.120 (58)	1.5497	.017[.015,.019]	.995	.994	.015	254,623.125	254,808.084
	b2. unidirectional(depression → Δ ADHD)	242. 370 (58)	1.5502	.016[.014,.019]	.996	.994	.014	254,600.390	254,785.349
	b3. full coupling depression – ADHD	239.264 (57)	1.5493	.016[.014,.019]	.996	.994	.014	254,597.358	254,786.521
	c1. unidirectional(ADHD → Δ school records)	258.594 (58)	1.5443	.017[.015,.019]	.995	.994	.015	254,624.016	254,808.976
	c2. unidirectional(school records → Δ ADHD)	254.285 (58)	1.5468	.017[.015,.019]	.995	.994	.015	254,617.984	254,802.943
	c3. full coupling SR – ADHD	253.032 (57)	1.5396	.017[.015,.019]	.995	.994	.015	254,616.221	254,805.384
	d. = combination of models b2. and c2.	241.100 (57)	1.5466	.016[.014,.019]	.996	.994	.015	254,599.554	254,788.718

School records and anxiety symptoms.

The fit criteria of the no coupling, unidirectional coupling, and full coupling models were not far apart (see Table 4), but the results of the SB-χ² tests showed that the no coupling model fitted the data best. More specifically, both unidirectional models (SB-χ² = 2.04; df = 1; p = .153; SB-χ² = 0.95; df = 1; p = .329) as well as the full coupling model (SB-χ² = 3.62; df = 2; p = .164) did not fit better than the no coupling model. The no coupling model appeared to fit best without any alternative model restrictions (setting the slope covariances at zero: SB-χ² = 6.01; df = 1; p = .014; slope intercept and covariances across constructs at zero: SB-χ² = 4.50; df = 1; p = .034 and SB-χ² = 24.67; df = 1; p < .01; all connections over time at zero: SB-χ² = 28.99; df = 3; p < .01).

In the no coupling model, the baseline scores for school records and anxiety were strongly related (σ = 0.095; p < .01). Both constant change components were related to each other (σ = 0.003; p = .035), showing that more decrease in anxiety was related to a slightly stronger improvement in grades over the years.

The baseline anxiety score was not significantly associated to the constant change component for school records (σ = − 0.015; p = .051). However, children with higher (poorer) grades at baseline showed stronger decrease in anxiety over the years (σ = −0.014; p < .01).

School records and depression symptoms.

Again, the fit criteria were not far apart (see Table 4), but the results of the SB-χ² tests showed that both unidirectional models (SB-χ² = 2.28; df = 1; p = .131 and SB-χ² = 2.98; df = 1; p = .084) as well as the full coupling model (SB-χ² = 5.81; df = 2; p = .055) did not fit better than the no coupling model. Setting the covariance between the intercept of depression and slope for school records at zero improved the model fit (SB-χ² = 1.94; df = 1; p = .164). Additionally setting the covariance between both slopes at zero did not improve the model fit (SB-χ² = 33.79; df = 1; p < .01), so that model a2 (see Table 4) was chosen as the optimal model.

In model a2, higher depression scores were related to lower achievement at baseline (σ = 0.102; p < .01). The constant change components for depression and grade were positively related (σ = 0.004; p < .01), indicating that children who showed a stronger decrease in depressive symptoms also demonstrated a stronger improvement in grades over time. Children with lower academic performance at baseline showed a stronger decrease in depression over time (σ = −0.105; p < .01).

The influence of ADHD symptomsSchool records and anxiety symptoms.

Among the trivariate latent change score models for anxiety, school records, and ADHD, the no coupling model showed the best fit. Each of the models for unidirectional or full coupling between anxiety and ADHD (SB-χ² = 0.50; df = 1; p = .479; SB-χ² = 1.79; df = 1; p = .181; SB-χ2 = 2.07; df = 2; p = .356 for models b1 to b3 [see Table 4], respectively) or between school records and ADHD (SB-χ² = 0.55; df = 1; ^p = .457; SB-χ² = 3.64; df = 1; p = .056; SB-χ² = 3.80; df = 2; p = .150 for models c1 to c3 [see Table 4], respectively) did not fit better. This means that ADHD symptoms did not influence subsequent changes in school records or anxiety, or vice versa.

In the resulting trivariate model with no coupling, higher initial ADHD scores as well as more increase in ADHD over time were related to lower grades at baseline (σ = 0.236; p < .01 and σ = 0.033; p = .019, respectively). More anxiety was related to more ADHD symptoms at baseline (σ = 0.393; p < .01). Higher initial ADHD scores appeared related to less decrease in anxiety over time (σ = −0.032; p < .01) and increases in ADHD scores over time were related to increases in anxiety symptoms over time (σ = 0.007; p < .01). There were no major changes in the model results for the relation between anxiety and school records compared to the model without ADHD.

School records and depression symptoms.

When focusing on depression symptoms, the trivariate model with school records and ADHD fitted better when including a unidirectional coupling with depression symptoms influencing subsequent changes in ADHD (model b2, see Table 4), compared to the no coupling model (SB-χ² = 16.20; df = 1; p < .01). Additional coupling between ADHD and subsequent changes in depression (SB-χ² = 3.14; df = 1; p = .076) or between school records and ADHD (SB-χ² = 1.62; df = 1; p = .204) did not result in a better fit compared to model b2.

With respect to the relation between ADHD and the depression score at baseline, higher depression scores were related to higher ADHD scores at baseline (σ = 0.339; p < .01), but not to changes in ADHD over time (σ = −0.018; p = .649). Depression symptoms predicted subsequent changes in ADHD (γ = 0.171; p < .01). As mentioned above, an effect from ADHD to school records or depression at the next time point was not found (e.g., model b2 was chosen). There were no major changes in the model results for the relation between depression and school records compared to the model without ADHD.

Discussion

The aim of the current study was to evaluate the trivariate longitudinal relation between school records, symptoms of anxiety/ depression and of ADHD in children between about 9 and 12 years old. Briefly summarized, the results showed that school records and symptoms of anxiety or depression were related, but school records did not predict these symptoms at the next timepoint, nor the other way around. ADHD was associated with both school records and symptoms of anxiety/ depression. Depression symptoms were a negative leading indicator of the subsequent changes in ADHD.

Research question 1 focused on the longitudinal development of each of the constructs. School records showed a positive linear trend over the four years, alongside negative autoregressive effects of equal magnitude between consecutive time points. In combination, this resulted in a relatively stable, slightly deteriorating trajectory. The hypothesized decline was thus found back, but, contrary to earlier findings [14,15], not only from grade 6 onwards. Anxiety symptoms showed a decrease with age and greater declines in children with higher baseline levels, confirming our hypothesis. For depression symptoms, we found an overall progressive increase in symptoms over time. This increase over time was smaller in children with higher baseline depression levels, hinting at a convergence of trajectories over time. These results confirm our hypothesis only partly. The increase in symptoms over time is as expected, but our results contradict those of earlier research, which found an increase in depression symptoms only starting at age 12, not depending on symptom levels at the previous time point [17] or at baseline [16]. Possibly, this shows that children have started to be vulnerable for depression already in elementary school. However, as the cohorts in the current study were not specifically grade-related, we could not evaluate the effects of school transition. The increase in depression already before the age of 12 could also reflect the effects of the COVID-19 pandemic, which started during the data collection.

Although not the focus of our hypotheses, the current study’s results do show gender differences. More specifically, as would be expected, females had lower baseline levels of ADHD. The decrease in symptoms over time was slightly smaller than for males. For depression, females had lower baseline levels, which does not correspond to the frequent finding that females, on average, exhibit more internalizing problems than males [16,56]. However, females showed a stronger increase in symptoms over time than males, resulting in higher symptom levels at the last time point. Anxiety symptoms showed a linear decline over time, which was stronger for males than for females, with no differences in baseline levels. The results of previous longitudinal studies are mixed. For example, McLaughlin and King [16] did not find gender differences in internalizing symptom trajectories. Ohannessian and colleagues [57], however, found a decrease in anxiety disorders in females but not males in middle to late adolescence, which is somewhat comparable to our results. Maybe the relatively young age of the children in our sample (9–12 years) can explain why we did not find higher initial rates for females, as the gender difference emerges only around age 12, at least for depression [58,59], and depression symptoms appear to accelerate earlier in adolescence for females compared to males [60]. However, this does not explain the lower baseline depression levels for females. Clearly, more research into gender differences in internalizing symptom trajectories is needed.

For both anxiety and depression symptoms, the results for research question 2 showed no coupling with school records. In other words, symptoms did not predict school records at the next time point, nor the other way around. This goes against our hypotheses as well as earlier research that showed low achievement to predict higher anxiety levels in the next school year [29,61]. As expected, we did find a negative correlation between symptoms and school records at baseline. Also, the results showed that poorer grades at baseline were related to a stronger decline in anxiety and depression symptoms over the years. Altogether, this means that the level of school records on itself, not its development, is related to anxiety and depression symptoms. This could partly be explained by the way school records develop over time, with improvements overall, but negative effects of one time point to the next. These effects could cancel each other out, resulting in relatively stable school records. Also, although some studies do find a negative relation between anxiety and achievement, some studies actually find a positive relation [e.g., 40], which means that it is likely that other variables play an important role.

Research question 3 focused on the role of ADHD symptoms. For the relation with school records, the results supported our hypotheses in that more ADHD symptoms at baseline were related to poorer grades at baseline. However, we did not find the expected increasing disadvantage caused by ADHD symptoms influencing changes in school records at the next time point. Also as expected, more ADHD symptoms at baseline were related to more internalizing symptoms at baseline as well as a smaller decrease in internalizing symptoms over time. Depression symptoms appeared to predict subsequent ADHD levels, but not the other way around. Contrary to the results of Murray et al. [37], ADHD symptoms did not influence anxiety levels at the next time point, nor the other way around. The study by Murray and colleagues contained a sample of children between 13 and 17 years of age. Possibly, symptoms of both ADHD and anxiety are more stable among younger children and start influencing each other more during adolescence. Another possible explanation is that influences from one time point to the next are dependent on specific ages, as the samples per time point in our study were not as homogeneous in terms of age as those in the study of Murray and colleagues due to the cross-sequential design.

Our hypotheses stated that ADHD symptoms would explain the relation between school records and subsequent anxiety or depression, as well as (for depression) the other way around. This hypothesis could not be evaluated, because these relations were not found in the first place. More in general, there were no clear differences between the models with and without ADHD symptoms in the relations found between school records and anxiety/ depression symptoms. This is not in line with the results of earlier research, which found that depression was more strongly (negatively) related to school records in students with ADHD, compared to those without [6,40]. Also, ADHD symptoms completely accounted for the relation found between reading/ spelling achievement and anxiety symptoms [6].

These contradictory results show the complexity of the underlying phenomena and relations. ADHD symptoms could actually play a role, but it is likely just one of the variables in play. For example, although ADHD symptoms and internalizing problems are clearly related, symptoms of anxiety and/or depression can also arise in the absence of ADHD symptoms and can in that case still be associated with school records. The extent to which school records are affected could depend, for example, on the extent to which the environment of students (teachers, parents) recognizes their symptoms and offer support.

The current study has a number of limitations. First, as mentioned, the ABCD study has a cross-sequential design, as opposed to a cohort-sequential design. The children were 9 years old at the start of the study on average, but the range in both age (8;11–11;1 years) and grade (3–5 for 96% of the sample) was relatively broad. We did not take into account these age and grade differences within cohorts, which means that we could have missed certain age- or grade-specific effects. A cross-sequential design does allow for the evaluation of cohort effects, but because the time range of the baseline measure was broad as well (2016–2018), it was also difficult to take into account the effects of certain events.

This brings us to the second limitation: The influence of the COVID-19 pandemic, at least for part of the sample. The COVID-19 pandemic led to an increase in mental health problems in children and adolescents, especially internalizing symptoms [e.g., 62]. This was also reflected in the sample of the ABCD study, although the increased depression scores were still within the normal range [63]. This will likely have had an influence, especially on the results for research question 1. Due to the complexity of the analyses in terms of the number of variables included, we chose not to take into account the start of the pandemic in our analyses. If it was indeed a confounding variable, this could form part of the explanation why we found earlier increases in depression than expected. It could also explain the different proportional change component for depression from the first to the second time point, as COVID-19 took place at the start of the study. In addition, it might have led to an underestimation of the coupling between depression and school records and/ or ADHD.

Third, we operationalized learning outcomes as the child’s school grade in the past year as reported by the parents. Although these parent-reports have been shown to be less valid for children with non-average learning performance, research has supported their use [64]. However, they are not as valid as objective measures of school grades had been. In addition, we used this ordinal variable as a continuous variable in the analyses. This could have resulted in bias in the form of underestimation of factor loadings, but less so in the estimation of structural relationships between the variables [65]. Also, the use of parent-reported ADHD- and internalizing symptoms is a limitation, because these do not always correspond to self-reported symptoms [66,67] and might underestimate especially internalizing symptoms [68]. The at times non-optimal operationalization of variables is inherent to the use of an existing data set.

Fourth and finally, we used the data from a large study in the USA, where we as researchers do not live ourselves and we were not involved in the data collection. We are thus far removed from the actual object of study and can assess the context factors that might have played a role not as well as a USA resident. However, the study has been logged in a detailed way, so that we were well able to use the data and properly describe the method of study. More in general, the use of the data from the ABCD study has enabled us to evaluate developmental trajectories in a large representative sample, resulting in a high degree of generalizability of the results. The availability of four time points and use of LCSM formed a methodological strength as within person changes are decomposed into different growth components.

Even though we did not find ADHD to explain the relation between school records and internalizing problems, we did find clear associations between school records, internalizing problems, and ADHD symptoms. This is in line with the notion that comorbidity between neurodevelopmental disorders is the rule rather than the exception [69] and with earlier findings on the comorbidity between SLDs and internalizing problems [e.g., 4,5]. More longitudinal studies like the current one are needed to unravel how mental health symptoms and school results develop in relation to each other.

The results of the current study help putting the puzzle together of how neurodevelopmental disorders and their comorbidities can be explained based on the multiple deficit model. This, in turn, can help identifying children with increased risk at an early stage. The fact that we did not find symptoms of depression or anxiety to predict school results at a later time point or vice versa might indicate that they do not influence each other directly. Apart from the finding that depression symptoms predicted subsequent changes in ADHD symptoms, a confounding role of ADHD symptoms in the relation with school records was not found. Therefore, other risk factors, not taken into account in the current study, might be responsible for difficulties in each of the domains. Future research is needed to identify these risk factors. For example, executive function deficits do not only play a role in both SLD and ADHD [70], but are also related to symptoms of depression and anxiety [71]. Although the current study focuses solely on child-related factors, based on the transactional model, child development is the result from a continuous bidirectional effect between the child and its environment [72]. Therefore, risk factors in the environment of the child are essential to take into account as well. For example, parenting characteristics appear to play a role in explaining internalizing problems [73], ADHD symptoms [73] as well as the coping strategies in children with SLD [74].

Altogether, our results showed that school records, internalizing symptoms (anxiety, depression), and ADHD symptoms were all related. However, we did not find direct influences on each other from time point to time point in our longitudinal models, with one exception: Depression symptoms can lead to subsequent increases in ADHD symptoms. These results yield practical recommendations for both clinical and educational practice. In clinical practice, it is essential to be aware of increased risks for ADHD symptoms when a child has depression symptoms. Also, if a child shows increases in ADHD symptoms, this can be a sign of prior depressive symptoms. An advice for teachers is to be aware of an increased risk for internalizing symptoms especially in the case of low grades overall, as opposed to decreasing school results. Also, it is important to recognize signs of depression at an early stage, so that such secondary problems (i.e., ADHD symptoms) can be prevented. Knowledge on such indicators of internalizing problems can be very helpful for increasing the early identification rate. As the name implies, internalizing symptoms are directed inward and therefore relatively hard to recognize for important others, such as parents and teachers. Early identification is, however, essential because internalizing symptoms are related to increased risk for other mental health problems [e.g., 75] and costs for the society [76].

Supporting information

S1 TableSample Sizes Separately per Time Point, Variable, and by Gender.

(DOCX)

We would like to thank Prof. Florian Schmiedek from the DIPF for his repeated methodological support, which has been essential for this paper. In addition, we would like to thank the ABCD Research Consortium for sharing their data via the National Institute of Mental Health (NIMH) Data Archive (NDA).

Abbreviations:

ADHD

attention deficit hyperactivity disorder

References1

American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. 2013. Available: doi:

10.1176/appi.books.9780890425596

Cadieux

, Syeda

. Meeting the Mental Health Needs of Students with Specific Learning Disorder: Recommendations to Advance and Innovate Current School-Based Approaches. Ontario, Canada: Centre for School Mental Health, Western University; 2021. Available: https://www.csmh.uwo.ca/docs/publications/isulabpublications/evidence%20brief%20-%20specific%20learning%20disorders.pdf

DuPaul

, Gormley

, Laracy

. Comorbidity of LD and ADHD: implications of DSM-5 for assessment and treatment. J Learn Disabil. 2013;46(1):43–51. doi:

10.1177/0022219412464351

23144063

Donolato

, Cardillo

, Mammarella

, Melby-Lervåg

. Research Review: Language and specific learning disorders in children and their co-occurrence with internalizing and externalizing problems: a systematic review and meta-analysis. J Child Psychol Psychiatry. 2022;63(5):507–18. doi:

10.1111/jcpp.13536

34747025

Visser

, Kalmar

, Linkersdörfer

, Görgen

, Rothe

, Hasselhorn

, et al. Comorbidities between specific learning disorders and psychopathology in elementary school children in Germany. Front Psychiatry. 2020;11:292. doi:

10.3389/fpsyt.2020.00292

32411022

PMC7198840

Visser

, Linkersdörfer

, Rothe

, Görgen

, Hasselhorn

, Gerd Schulte-Körne

. The role of ADHD symptoms in the relationship between academic achievement and psychopathological symptoms. Res Dev Disabil. 2020;97:103552. doi:

10.1016/j.ridd.2019.103552

31884314

Ipekian

. Comorbidity in Adults with Attention Deficit Hyperactivity Disorder with Math and Reading Specific Learning Disorders. Master, McGill University. 2021. Available: https://search.proquest.com/openview/27c81d1afb3e3bb64c8f8933d1281dd1/1?pq-origsite=gscholar&cbl=18750&diss=y

McGrath

, Peterson

, Pennington

. The Multiple Deficit Model: Progress, Problems, and Prospects. Sci Stud Read. 2020;24(1):7–13. doi:

10.1080/10888438.2019.1706180

32440085

PMC7241589

Patalay

, Deighton

, Fonagy

, Wolpert

. The relationship between internalising symptom development and academic attainment in early adolescence. PLoS One. 2015;10(1):e0116821. doi:

10.1371/journal.pone.0116821

25607541

PMC4301632

Papaioannou

, Mouzaki

, Sideridis

, Antoniou

, Padeliadu

, Simos

. Cognitive and academic abilities associated with symptoms of attention-deficit/hyperactivity disorder: a comparison between subtypes in a Greek non-clinical sample. Educ Psychol. 2016;36:138–58. doi:

10.1080/01443410.2014.915931

Pollak

. Developmental psychopathology: recent advances and future challenges. World Psychiatry. 2015;14(3):262–9. doi:

10.1002/wps.20237

26407771

PMC4592638

Steinberg

. Cognitive and affective development in adolescence. Trends Cogn Sci. 2005;9(2):69–74. doi:

10.1016/j.tics.2004.12.005

15668099

, Allen

, Casillas

. Relating psychological and social factors to academic performance: A longitudinal investigation of high-poverty middle school students. J Adolesc. 2017;56:179–89. doi:

10.1016/j.adolescence.2017.02.007

28273555

Duckworth

, Tsukayama

, May

. Establishing causality using longitudinal hierarchical linear modeling: an illustration predicting achievement from self-control. Soc Psychol Personal Sci. 2010;1:311–7. doi:

10.1177/1948550609359707

20976121

PMC2957016

Ryan

, Shim

, Makara

. Changes in academic adjustment and relational self-worth across the transition to middle school. J Youth Adolesc. 2013;42(9):1372–84. doi:

10.1007/s10964-013-9984-7

23873280

McLaughlin

, King

. Developmental trajectories of anxiety and depression in early adolescence. J Abnorm Child Psychol. 2015;43(2):311–23. doi:

10.1007/s10802-014-9898-1

24996791

PMC4286282

Cohen

, Andrews

, Davis

, Rudolph

. Anxiety and Depression During Childhood and Adolescence: Testing Theoretical Models of Continuity and Discontinuity. J Abnorm Child Psychol. 2018;46(6):1295–308. doi:

10.1007/s10802-017-0370-x

29256025

PMC6008170

Ormel

, Oldehinkel

, Sijtsema

, van Oort

, Raven

, Veenstra

, et al. The TRacking Adolescents’ Individual Lives Survey (TRAILS): design, current status, and selected findings. J Am Acad Child Adolesc Psychiatry. 2012;51(10):1020–36. doi:

10.1016/j.jaac.2012.08.004

23021478

, Huebner

, Tian

. Co-Developmental Trajectories of Specific Anxiety Symptoms from Middle Childhood to Early Adolescence: Associations with Psychological Well-Being and Academic Achievement. J Youth Adolesc. 2021;50(6):1140–56. doi:

10.1007/s10964-021-01411-5

33675506

Schubert

, Clark

, Van

, Collinson

, Baune

. Depressive symptom trajectories in late adolescence and early adulthood: A systematic review. Aust N Z J Psychiatry. 2017;51(5):477–99. doi:

10.1177/0004867417700274

28415879

Ferro

, Gorter

, Boyle

. Trajectories of Depressive Symptoms in Canadian Emerging Adults. Am J Public Health. 2015;105(11):2322–7. doi:

10.2105/AJPH.2015.302817

26378840

PMC4605152

Conway

, Zinbarg

, Mineka

, Craske

. Core dimensions of anxiety and depression change independently during adolescence. J Abnorm Psychol. 2017;126(2):160–72. doi:

10.1037/abn0000222

28192011

PMC5308556

Brumariu

, Waslin

, Gastelle

, Kochendorfer

, Kerns

. Anxiety, academic achievement, and academic self-concept: Meta-analytic syntheses of their relations across developmental periods. Dev Psychopathol. 2022;1–17. doi:

10.1017/S0954579422000323

35491696

Morin

AJS

, Maïano

, Nagengast

, Marsh

, Morizot

, Janosz

. General growth mixture analysis of adolescents’ developmental trajectories of anxiety: The impact of untested invariance assumptions on substantive interpretations. Struct Equ Model Multidiscip J. 2011;18:613–48. doi:

10.1080/10705511.2011.607714

Wickersham

, Sugg

HVR

, Epstein

, Stewart

, Ford

, Downs

. Systematic Review and Meta-analysis: The Association Between Child and Adolescent Depression and Later Educational Attainment. J Am Acad Child Adolesc Psychiatry. 2021;60(1):105–18. doi:

10.1016/j.jaac.2020.10.008

33130250

PMC7779367

Huang

. Academic Achievement and Subsequent Depression: A Meta-analysis of Longitudinal Studies. J Child Fam Stud. 2015;24(2):434–42. doi:

10.1007/s10826-013-9855-6

Hishinuma

, Chang

, McArdle

, Hamagami

. Potential causal relationship between depressive symptoms and academic achievement in the Hawaiian high schools health survey using contemporary longitudinal latent variable change models. Dev Psychol. 2012;48(5):1327–42. doi:

10.1037/a0026978

22268606

PMC3339048

Zhang

, Zhang

, Chen

, Ji

, Deater-Deckard

. Developmental changes in longitudinal associations between academic achievement and psychopathological symptoms from late childhood to middle adolescence. J Child Psychol Psychiatry. 2019;60(2):178–88. doi:

10.1111/jcpp.12927

29741777

Weidman

, Augustine

, Murayama

, Elliot

. Internalizing symptomatology and academic achievement: Bi-directional prospective relations in adolescence. J Res Personal. 2015;58:106–14. doi:

10.1016/j.jrp.2015.07.005

Chen

, Huebner

, Tian

. Longitudinal associations among academic achievement and depressive symptoms and suicidal ideation in elementary schoolchildren: disentangling between- and within-person associations. Eur Child Adolesc Psychiatry. 2022;31(9):1405–18. doi:

10.1007/s00787-021-01781-y

33885993

Brittain

, Vaillancourt

. Longitudinal associations between academic achievement and depressive symptoms in adolescence: Methodological considerations and analytical approaches for identifying temporal priority. Adv Child Dev Behav. 2023;64:327–55. doi:

10.1016/bs.acdb.2022.11.003

37080673

Larsson

J-O

, Larsson

, Lichtenstein

. Genetic and environmental contributions to stability and change of ADHD symptoms between 8 and 13 years of age: a longitudinal twin study. J Am Acad Child Adolesc Psychiatry. 2004;43(10):1267–75. doi:

10.1097/01.chi.0000135622.05219.bf

15381894

Willcutt

, Petrill

, Wu

, Boada

, DeFries

, Olson

. Comorbidity between reading disability and math disability: concurrent psychopathology, functional impairment, and neuropsychological functioning. J Learn Disabil. 2013;46:500–16. doi:

10.1177/0022219413477476

23449727

PMC3749272

Ehm

J-H

, Kerner Auch Koerner

, Gawrilow

, Hasselhorn

, Schmiedek

. The association of ADHD symptoms and reading acquisition during elementary school years. Dev Psychol. 2016;52(9):1445–56. doi:

10.1037/dev0000186

27570983

Lawrence

, Houghton

, Dawson

, Sawyer

, Carroll

. Trajectories of academic achievement for students with attention-deficit/hyperactivity disorder. Br J Educ Psychol. 2021;91(2):755–74. doi:

10.1111/bjep.12392

33259064

Park

, Seo

, Park

, Kim

, Choi

. The influence of behavioral and emotional characteristics on academic achievement of middle school students: A growth modeling approach. Sch Psychol Int. 2019;40:433–55. doi:

10.1177/0143034319853010

Murray

, Caye

, McKenzie

, Auyeung

, Murray

, Ribeaud

, et al. Reciprocal Developmental Relations Between ADHD and Anxiety in Adolescence: A Within-Person Longitudinal Analysis of Commonly Co-Occurring Symptoms. J Atten Disord. 2022;26(1):109–18. doi:

10.1177/1087054720908333

32172640

Schatz

, Rostain

. ADHD with comorbid anxiety: a review of the current literature. J Atten Disord. 2006;10(2):141–9. doi:

10.1177/1087054706286698

17085624

Gair

, Brown

, Kang

, Grabell

, Harvey

. Early Development of Comorbidity Between Symptoms of ADHD and Anxiety. Res Child Adolesc Psychopathol. 2021;49(3):311–23. doi:

10.1007/s10802-020-00724-6

33404952

PMC7878348

DuPaul

, Gormley

, Anastopoulos

, Weyandt

, Labban

, Sass

, et al. Academic Trajectories of College Students with and without ADHD: Predictors of Four-Year Outcomes. J Clin Child Adolesc Psychol. 2021;50(6):828–43. doi:

10.1080/15374416.2020.1867990

33529049

PMC8797030

Visser

, Brandenburg

. The longitudinal relationship between learning outcomes and internalising symptoms in early adolescence: The role of ADHD symptoms. Open Science Framework; 2022 [cited 2023 Aug 21]. Available: https://osf.io/yw5jv/

Leadbeater

, Thompson

, Gruppuso

. Co-occurring trajectories of symptoms of anxiety, depression, and oppositional defiance from adolescence to young adulthood. J Clin Child Adolesc Psychol. 2012;41(6):719–30. doi:

10.1080/15374416.2012.694608

22742519

PMC4905756

Daviss

. A review of co-morbid depression in pediatric ADHD: etiology, phenomenology, and treatment. J Child Adolesc Psychopharmacol. 2008;18(6):565–71. doi:

10.1089/cap.2008.032

19108661

PMC2699665

Powell

, Riglin

, Hammerton

, Eyre

, Martin

, Anney

, et al. What explains the link between childhood ADHD and adolescent depression? Investigating the role of peer relationships and academic attainment. Eur Child Adolesc Psychiatry. 2020;29(11):1581–91. doi:

10.1007/s00787-019-01463-w

31932968

PMC7595988

ABCD Study. About - ABCD Study. 2021. Available: https://abcdstudy.org/about/

Garavan

, Bartsch

, Conway

, Decastro

, Goldstein

, Heeringa

, et al. Recruiting the ABCD sample: Design considerations and procedures. Dev Cogn Neurosci. 2018;32:16–22. doi:

10.1016/j.dcn.2018.04.004

29703560

PMC6314286

Auchter

, Hernandez Mejia

, Heyser

, Shilling

, Jernigan

, Brown

, et al. A description of the ABCD organizational structure and communication framework. Dev Cogn Neurosci. 2018;32:8–15. doi:

10.1016/j.dcn.2018.04.003

29706313

PMC6462277

Achenbach

, Rescorla

. Manual for the ASEBA school-age forms & profile: An integrated system of multi-informant assessment. Burlington (VT): University of Vermont, Research Center for Children, Youth, and Families; 2001. Available: https://scholar.google.com/citations?user=gfivlciaaaaj&hl=nl&oi=sra

Reynolds

, Kamphaus

. Behavior assessment system for children (BASC) manual. Circle Pines: American Guidance Service; 1992.

Conners

. Conners’ Parent Rating Scale--Revised. North Tonawanda (NY): Multi-Health Systems; 1997.

RStudio Team. RStudio: Integrated Development Environment for R. Boston (MA): RStudio, PBC; 2020.

Muthén LK& M BO. (1998-2017). Mplus User’s Guide. Eighth Edition ed. Los Angeles (CA): Muthén & Muthén; 2017.

Lövdén

, Ghisletta

, Lindenberger

. Social participation attenuates decline in perceptual speed in old and very old age. Psychol Aging. 2005;20(3):423–34. doi:

10.1037/0882-7974.20.3.423

16248702

Ullman

JB.

Using multivariate statistics. In: Tabachnick

, Fidell

, editors. Structural equation modeling. Needham Heights (MA): Allyn and Bacon; 2013. p. 681–785.

Mplus. Chi-Square Difference Testing Using the Satorra-Bentler Scaled Chi-Square. [cited 21 Mar 2024]. Available: http://www.statmodel.com/chidiff.shtml

Rescorla

, Ivanova

, Achenbach

, Begovac

, Chahed

, Drugli

. International epidemiology of child and adolescent psychopathology II: Integration and applications of dimensional findings from 44 societies. J Am Acad Child Adolesc Psychiatry. 2012;51:1273-1283.e8. doi:

10.1016/j.jaac.2012.09.012

23200284

Ohannessian

, Milan

, Vannucci

. Gender Differences in Anxiety Trajectories from Middle to Late Adolescence. J Youth Adolesc. 2017;46(4):826–39. doi:

10.1007/s10964-016-0619-7

27889856

PMC5815170

Salk

, Hyde

, Abramson

. Gender differences in depression in representative national samples: Meta-analyses of diagnoses and symptoms. Psychol Bull. 2017;143(8):783–822. doi:

10.1037/bul0000102

28447828

PMC5532074

Hankin

, Young

, Abela

JRZ

, Smolen

, Jenness

, Gulley

, et al. Depression from childhood into late adolescence: Influence of gender, development, genetic susceptibility, and peer stress. J Abnorm Psychol. 2015;124(4):803–16. doi:

10.1037/abn0000089

26595469

PMC4662048

Salk

, Petersen

, Abramson

, Hyde

. The contemporary face of gender differences and similarities in depression throughout adolescence: Development and chronicity. J Affect Disord. 2016;205:28–35. doi:

10.1016/j.jad.2016.03.071

27391269

PMC5468750

Robidoux

, Eklund

, McArthur

, Francis

, Aro

, Torppa

. Reading and emotional-behavioural development in Finnish children: a longitudinal study of associations. Read Writ. 2024. doi:

10.1007/s11145-024-10559-y

Ravens-Sieberer

, Kaman

, Erhart

, Devine

, Schlack

, Otto

. Impact of the COVID-19 pandemic on quality of life and mental health in children and adolescents in Germany. Eur Child Adolesc Psychiatry. 2021;31(6):879–89. doi:

10.1007/s00787-021-01726-5

33492480

PMC7829493

Hamatani

, Hiraoka

, Makita

, Tomoda

, Mizuno

. Longitudinal impact of COVID-19 pandemic on mental health of children in the ABCD study cohort. Sci Rep. 2022;12(1):19601. doi:

10.1038/s41598-022-22694-z

36379997

PMC9665012

Gilger

. Using self-report and parental-report survey data to assess past and present academic achievement of adults and children. J Appl Dev Psychol. 1992;13:235–56. doi:

10.1016/0193-3973(92)90031-C

C-H

. Statistical estimation of structural equation models with a mixture of continuous and categorical observed variables. Behav Res Methods. 2021;53(5):2191–213. doi:

10.3758/s13428-021-01547-z

33791955

Kaurin

, Egloff

, Stringaris

, Wessa

. Only complementary voices tell the truth: a reevaluation of validity in multi-informant approaches of child and adolescent clinical assessments. J Neural Transm Vienna Austria 1996. 2016;123(8):981–90. doi:

10.1007/s00702-016-1543-4

27118025

van de Looij-Jansen

, Jansen

, de Wilde

, Donker

MCH

, Verhulst

. Discrepancies Between Parent-Child Reports of Internalizing Problems Among Preadolescent Children: Relationships with Gender, Ethnic Background, and Future Internalizing Problems. J Early Adolesc. 2011;31(3):443–62. doi:

10.1177/0272431610366243

Lagattuta

, Sayfan

, Bamford

. Do you know how I feel? Parents underestimate worry and overestimate optimism compared to child self-report. J Exp Child Psychol. 2012;113(2):211–32. doi:

10.1016/j.jecp.2012.04.001

22727673

Braet

, Prins

, Bijttebier

Ontwikkeling en psychopathologie. In: Prins

, Braet

, editors. Handboek klinische ontwikkelingspsychologie. 2nd ed. Houten: Bohn Stafleu van Loghum; 2014. p. 3–56.

Crisci

, Caviola

, Cardillo

, Mammarella

. Executive Functions in Neurodevelopmental Disorders: Comorbidity Overlaps Between Attention Deficit and Hyperactivity Disorder and Specific Learning Disorders. Front Hum Neurosci. 2021;15:594234. doi:

10.3389/fnhum.2021.594234

33732121

PMC7958764

Han

, Helm

, Iucha

, Zahn-Waxler

, Hastings

, Klimes-Dougan

. Are Executive Functioning Deficits Concurrently and Predictively Associated with Depressive and Anxiety Symptoms in Adolescents? J Clin Child Adolesc Psychol. 2016;45(1):44–58. doi:

10.1080/15374416.2015.1041592

26042358

PMC4670287

Sameroff

. The transactional model of development: How children and contexts shape each other. The transactional model. Washington, DC: American Psychological Association; 2009. p. 3–21.

Yap

MBH

, Jorm

. Parental factors associated with childhood anxiety, depression, and internalizing problems: a systematic review and meta-analysis. J Affect Disord. 2015;175:424–40. doi:

10.1016/j.jad.2015.01.050

25679197

Enagandula

, Singh

, Subramanyam

, Kamath

. Study of parenting styles, family functioning and its relation to coping strategies in children of specific learning disorder. 2020 [cited 16 Oct 2025]. Available: http://imsear.searo.who.int/handle/123456789/204435

Avenevoli

, Swendsen

, He

J-P

, Burstein

, Merikangas

. Major depression in the national comorbidity survey-adolescent supplement: prevalence, correlates, and treatment. J Am Acad Child Adolesc Psychiatry. 2015;54(1):37-44.e2. doi:

10.1016/j.jaac.2014.10.010

25524788

PMC4408277

Donohue

, Pincus

. Reducing the societal burden of depression: a review of economic costs, quality of care and effects of treatment. PharmacoEconomics. 2007;25(1):7–24. doi:

10.2165/00019053-200725010-00003

17192115

10.1371/journal.pone.0347582.r001

Decision Letter 0

Chen

Mu-Hong

Academic Editor

2026

Mu-Hong Chen

https://creativecommons.org/licenses/by/4.0/

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Submission Version

13 Oct 2025

Dear Dr. Janin Brandenburg,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Nov 27 2025 11:59PM If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols....

We look forward to receiving your revised manuscript.

Kind regards,

Mu-Hong Chen, M.D., Ph.D.

Academic Editor

PLOS ONE

Journal requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We note that you have indicated that there are restrictions to data sharing for this study. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For more information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

Before we proceed with your manuscript, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., a Research Ethics Committee or Institutional Review Board, etc.). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of recommended repositories, please see

https://journals.plos.org/plosone/s/recommended-repositories. You also have the option of uploading the data as Supporting Information files, but we would recommend depositing data directly to a data repository if possible.

We will update your Data Availability statement on your behalf to reflect the information you provide.

3. We notice that your supplementary tables are included in the manuscript file. Please remove them and upload them with the file type 'Supporting Information'. Please ensure that each Supporting Information file has a legend listed in the manuscript after the references list.

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

Reviewer #1: To the Authors,

Thank you for the opportunity to review your interesting and methodologically robust study. This note is to explain the reasoning behind my answers to the review questions.

While the manuscript is clearly written and the statistical analyses are appropriate and rigorous (addressing questions 2 & 4), I have indicated that the data only partly support the conclusions at this stage (addressing question 1). This is due to three main reasons:

Measurement Limitations: The use of a single, parent-reported item to measure "learning outcomes" is a significant limitation that impacts the strength of the conclusions drawn about this core construct.

Interpretation of Key Findings: The manuscript presents several significant and compelling gender differences in the results, but these are not interpreted or contextualized in the discussion. This omission leaves a major part of the findings unexplored.

Contextual Factors: The potential confounding impact of the COVID-19 pandemic on the developmental trajectories, particularly for depression, is noted as a limitation but is not sufficiently integrated into the main interpretation of the results.

Addressing these points by expanding the discussion around the limitations of the measurement, interpreting the gender differences, and more deeply considering the pandemic's role will substantially strengthen the manuscript and ensure the conclusions are more firmly supported by the data.

My full, detailed review with specific recommendations for revision is provided in the accompanying attachment. I believe that by addressing these comments, the manuscript will be an excellent contribution to the journal

Reviewer #2: This longitudinal study examined the relationships among adolescents’ learning outcomes, internalizing symptoms (anxiety and depression), and ADHD symptoms using four waves of data from the Adolescent Brain Cognitive Development (ABCD) Study involving 11,867 U.S. children aged 9–12 years. Latent change score modeling showed that academic performance correlated with anxiety and depression but did not predict changes in these symptoms over time. ADHD symptoms were associated with both poorer school records and higher internalizing symptoms; however, they did not explain their interrelation. Depression predicted subsequent increases in ADHD symptoms. The findings highlight the importance of early detection of depressive symptoms. This study was well-conducted and the manuscript is well-written. Some minor comments are listed as below.

1. The study uses four timepoints from the ABCD dataset and latent change score modeling (LCSM), which allows a nuanced examination of within-person changes. This is a methodological strength, particularly for studying developmental trajectories in a large, representative sample.

2. The hypotheses could be better grounded in specific theoretical frameworks (e.g., multiple deficit model or transactional models). The discussion might better integrate these theories to interpret the null findings, particularly why ADHD did not mediate the link between learning outcomes and internalizing symptoms.

3. The exclusive use of parent reports for ADHD, anxiety, and depression may limit validity. Adolescents’ self-reports or teacher evaluations could have provided complementary perspectives, especially for internalizing symptoms, which parents may underreport. This limitation deserves more emphasis in the discussion.

4. While the manuscript concludes with practical recommendations for teachers, these implications are only loosely connected to the actual analytic results. The paper could benefit from a clearer discussion on how observed statistical patterns translate into real-world educational or clinical practice.

**********

what does this mean?). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our Privacy Policy..-->

Reviewer #1: Yes: Dr. Chen Hanna RyderDr. Chen Hanna RyderDr. Chen Hanna RyderDr. Chen Hanna Ryder

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.. Please note that Supporting Information files do not need this step.. Please note that Supporting Information files do not need this step.. Please note that Supporting Information files do not need this step.

Attachment

Submitted filename: Peer Review of Manuscript PONE_PlosOne.docx

10.1371/journal.pone.0347582.r002

Author response to Decision Letter 1

Submission Version

12 Mar 2026

Response to reviewers

Journal requirements

In response to the request in the decision e-mail, we would like to explain the restrictions to data sharing for this study. We used data from the ABCD study provided by the NIMH. The signed Data Use Certification states:

“Recipients agree to retain control over data and to not distribute, sell, or move data, with or without charge, in any form, to any other individual, entity, or third-party system [...].”

Hence, the data does not belong to us / our institutions and we do not have the right to share the data. We did share all analysis scripts of our study (see https://osf.io/gw8d3/overview?view_only=abcea2aaa99d448a8b7cb068d9f030a0).

Response to reviewer 1

(responses in italics)

Dear Authors,

Thank you for the opportunity to review your manuscript, "The longitudinal relation between adolescents’ learning outcomes and internalizing symptoms: The role of ADHD symptoms". This is an important and timely article that addresses a complex and highly relevant issue. The use of the impressive ABCD Study dataset and the application of sophisticated latent change score models are significant strengths.

Thank you for the extensive review and valuable feedback on our paper.

This note is to explain the reasoning behind my answers to the review questions and to provide detailed feedback for revision. While the manuscript is clearly written and the statistical analyses are appropriate and rigorous, I have indicated that the data only partly support the conclusions at this stage. This assessment is based on several key areas that, I believe, require revision to strengthen the manuscript's overall impact and clarity. My detailed comments are provided below.

Major Points:

1. Operationalization and Measurement of "Learning Outcomes": The sole measure for learning outcomes is a single parent-report item on their child's school grades. While acknowledging the constraints of large-scale datasets, this is a significant limitation that impacts the strength of the conclusions drawn about this core construct.

o Recommendation: Please expand the discussion on this limitation. Specifically, address the potential biases of parent-reported grades versus more objective measures and discuss the implications of treating this ordinal variable as continuous in the models. Furthermore, for consistency, consider changing the keyword "academic achievement" to "parent-reported school grades" or "school records," which are the terms used more consistently throughout the manuscript.

We agree that this operationalization is a limitation and should be discussed accordingly. We have added a paragraph to the discussion (see p. 33-34) and replaced the term “academic achievement” with “school records”(in the keywords as well as the “Data and sample”-section).

2. Interpretation of Key Findings: The manuscript presents several significant and compelling gender differences in the results, but these are not interpreted or contextualized in the discussion. This omission leaves a major part of the findings unexplored.

o Recommendation: The Discussion section should be expanded to include an interpretation of these gender differences. What are the potential theoretical or clinical implications of these findings? For instance, the finding that females show a larger decrease in both anxiety and depression symptoms warrants further exploration in the context of existing literature on gender differences in adolescent mental health.

We did not discuss these gender differences, as they were not the focus of our hypotheses. However, we do agree that this is an interesting part of the results and have therefore added a paragraph to the Discussion section in which we interpret these findings and make a connection with existing literature, see p. 30-31.

3. Contextual Factors: The potential confounding impact of the COVID-19 pandemic on the developmental trajectories, particularly for depression, is noted as a limitation but is not sufficiently integrated into the main interpretation of the results.

o Recommendation: The authors should integrate this potential confounder more deeply into the interpretation of the results in the Discussion. This could help explain why the depression trajectory results contradicted both the authors' hypotheses and previous literature.

Based on this comment, we have noticed that the interpretation of the results with respect to the developmental trajectory for depression was incorrect. More specifically, we concluded that depression symptoms declined, but the descriptive statistics showed an increase. After careful consideration, we have found that the cause for this mistake was a not-optimal coding of gender in the datafile of 1 (male) and 2 (female). We have changed this to 0 and 1, respectively, and were then able to properly interpret the results, which actually showed an increase in depression symptoms over time and thus (partly) confirmed our hypothesis. We have reran all analyses with this adapted coding for gender, which explains the slight changes throughout the Results section. In addition, we have added figures of the developmental trajectories for ease of interpretation (Figure 1). In addition, we incorporated the COVID-pandemic in the interpretation of the results for depression, see p. 30.

4. Clarity of Hypothesis Testing Regarding ADHD's Role: The third research question hypothesized that ADHD symptoms would explain the relation between school records and internalizing symptoms. As the bivariate analyses found no longitudinal coupling between them, this hypothesis could not be directly tested. The current framing might be slightly misleading.

o Recommendation: Consider reframing the third research question and hypotheses in the Introduction. Instead of framing ADHD as an explanatory variable for a presumed relationship, it would be more accurate to state that the study aims to simultaneously examine the trivariate dynamic relationships among the three constructs.

We agree that hypotheses 3.a.iii and 3.b.ii were slightly misleading, because they assumed a certain result for the second research question. We therefore reformulated the aim (“Third, we evaluated the trivariate dynamic relation between ADHD-symptoms, school records and..”). With respect to the hypotheses, we chose to keep the wording such that ADHD-symptoms would explain the relationship, but explicitly added (“if a relation between school records and subsequent anxiety is found, ..”), to take out the misleading character. The reason why we did not want to change the hypotheses is that we prefer not to do so after finishing the study when another solution is available. Also, they connect well to the literature described in the Introduction.

Minor Points:

1. Framing in the Introduction: The introduction begins by discussing Specific Learning Disorder (SLD), but the study focuses on a continuum of symptoms in a general population sample.

o Recommendation: Please make the transition from the discussion of SLD to the study's focus on the continuum of learning outcomes smoother and more explicit.

You are right, this was not completely clear. We have now made the focus on the general population more explicit in the last paragraph of the first part of the introduction.

2. Data from T4: The methods section states that data from T4 were excluded because academic achievement data were missing.

o Recommendation: Please clarify if data for the other key variables (e.g., CBCL) were available at T4 and briefly justify the decision to stop at T3 for all variables rather than using models that can accommodate missing data.

We have added the following clarification: “Although data for the other variables were available, using alternative methods such as imputation was not an option, because school records formed an essential element in the analyses and were missing for all children at T4” (see p. 9).

3. Reporting of Model Fit: In several instances, model selection relied on the chi-squared difference test, even when an alternative model had similar or better fit indices (e.g., RMSEA/CFI).

o Recommendation: It would be helpful to add a brief sentence justifying the reliance on the chi-squared difference test in these specific cases, especially given the large sample size.

We acknowledge that the chi-squared difference test is often significant in the case of a large sample size. However, model comparison was additionally based on the fit indices and on the significance of the proportional change components. We have now better clarified this in the paper on p. 14.

To explain this in a bit more detail, we have summarized the results related to research question 1 in the following table:

Model chi-squared diff. test RMSEA CFI Proportional change components Decision

RQ1 school records d1 b2 c1 / d1 / d2 sign. d1

RQ1 anxiety b2 b2 / d2 d2 n.s. b2

RQ1 depression d3 d3 d3 sign. d3

RQ1 ADHD d3 c1 / d3 c1 / d2 / d3 sign. d3

For school records, the chosen model (d1) does not have the lowest RMSEA, but it does have the highest CFI (just as high as c1 and d2, but d1 is the more parsimonious of these three).

For anxiety, we indeed chose a model that did not have the highest CFI. However, either of the models d would not be a good choice, because the proportional change components were not significant, which speaks for the more parsimonious model b2.

For ADHD, the CFI hints at c1, d2 or d3, but d2 has a lower number of df and thus is less parsimonious. Apart from the chi-squared test and significance of the proportional change components, the AIC- and sa-BIC-values also favor model d3 above c1.

I believe that by addressing these comments, the manuscript will be an excellent contribution to the journal.

We think the adjustments made based on your comments have indeed improved the manuscript.

Response to reviewer 2

(responses in italics)

This longitudinal study examined the relationships among adolescents’ learning outcomes, internalizing symptoms (anxiety and depression), and ADHD symptoms using four waves of data from the Adolescent Brain Cognitive Development (ABCD) Study involving 11,867 U.S. children aged 9–12 years. Latent change score modeling showed that academic performance correlated with anxiety and depression but did not predict changes in these symptoms over time. ADHD symptoms were associated with both poorer school records and higher internalizing symptoms; however, they did not explain their interrelation. Depression predicted subsequent increases in ADHD symptoms. The findings highlight the importance of early detection of depressive symptoms. This study was well-conducted and the manuscript is well-written. Some minor comments are listed as below.

Thank you for the compliments and for your valuable feedback on our paper.

Thank you. We had already briefly mentioned this as a strength and have now slightly extended this part, see p. 34.

We have chosen to keep the introduction as it is in this respect, as we have specified the multiple deficit framework in the beginning and subsequently based the hypotheses on what is known in the empirical literature about the specific relations between the variables of interest. However, we agree that, in the Discussion, we need to come back to this theoretical framework from the Introduction, which was lacking. Therefore, we have added a paragraph at the end of the Discussion in which a link is made to both the multiple deficit model and the transactional model (see p. 34-35).

We have added the use of parent-report for (both school records and) symptoms of ADHD, anxiety, and depression as a limitation to the Discussion, see p. 33-34.

We did try to base the recommendations for teachers on the concrete results of the current study. However, the paragraph (which we now moved to the end of the Discussion) could be better framed, which we did. In addition, we now added recommendations not only for educational, but also for clinical practice, see p. 35-36.

Attachment

Submitted filename: Response to Reviewers_final.docx

10.1371/journal.pone.0347582.r003

Decision Letter 1

Chen

Mu-Hong

Academic Editor

2026

Mu-Hong Chen

https://creativecommons.org/licenses/by/4.0/

Submission Version

5 Apr 2026

The longitudinal relation between adolescents’ learning outcomes and internalizing symptoms: The role of ADHD symptoms

PONE-D-25-47753R1

Dear Dr. Janin Brandenburg,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support....

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Mu-Hong Chen, M.D., Ph.D.

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

Reviewer #1: Dear Authors, Thank you for your thorough and thoughtful revision. I am highly impressed by the diligence with which you addressed all of my comments. Your decision to re-examine the data, which led to uncovering the gender coding error, exemplifies excellent and rigorous scientific practice. The revised trajectories now make much more sense, and the expanded discussion regarding measurement limitations, gender differences, and the COVID-19 context has substantially strengthened the manuscript. You have fully resolved all my concerns, and I am very happy to recommend this manuscript for publication. Congratulations on a great piece of work!

Reviewer #2: Thank you for the revision of the article "The longitudinal relation between adolescents’ learning outcomes and internalizing symptoms: The role of ADHD symptoms". My comments have been adequately addressed.

**********

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Reviewer #1: Yes: Dr. Chen Hanna RyderDr. Chen Hanna RyderDr. Chen Hanna RyderDr. Chen Hanna Ryder

Reviewer #2: No

**********

10.1371/journal.pone.0347582.r004

Acceptance letter

Chen

Mu-Hong

Academic Editor

2026

Mu-Hong Chen

https://creativecommons.org/licenses/by/4.0/

PONE-D-25-47753R1

PLOS One

Dear Dr. Brandenburg,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS One. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

* All relevant supporting information is included in the manuscript submission,

* There are no issues that prevent the paper from being properly typeset

You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps.

Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

You will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing.

If we can help with anything else, please email us at customercare@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Mu-Hong Chen

Academic Editor

PLOS One