Large-scale Longitudinal Dataset Research Articles

Timing Matters: A Longitudinal Study Examining the Effects of Physical Activity Intensity and Timing on Adolescents' Mental Health Outcomes.

Physical activity is universally acknowledged for its benefits to mental health; however, the specific intensities and timings that best benefit adolescents' mental health, crucial due to their significant influence on daily schedules, have not been thoroughly investigated. This study addresses the substantive research gap by exploring the varied effects of physical activity intensity (light versus moderate to vigorous) and timing (weekdays versus weekends and holidays) on adolescent mental health. Utilizing a large-scale longitudinal dataset (NT1 = 84,054; NT2 = 44,623) from 158 schools, this research describes the current state of adolescent physical activity and investigates the effects of physical activity on mental health outcomes, including positive (i.e., life satisfaction, positive mental health) and negative indicators (depression, anxiety), over a 6-month period. Participants were adolescents aged 9-19 years (mean age = 12.73 ± 2.43 years, 48.9% female), with the analysis adjusted for potential confounding factors. Results showed that as adolescents grow older, their engagement in moderate-to-vigorous physical activity during both weekdays and weekends/holidays tends to decrease, while light physical activity during weekdays increases. Multilevel regression analysis indicated that moderate-to-vigorous physical activity during weekends/holidays at Time 1 positively correlated with better mental health outcomes at Time 2 (six months later), featuring enhanced positive indicators and reduced negative ones. Moderate-to-vigorous physical activity on weekdays at Time 1 was positively linked to mental health at Time 2. Light physical activity during weekends/holidays at Time 1 positively predicted life satisfaction and positive mental health at Time 2. In contrast, light physical activity on weekdays at Time 1 negatively correlated with life satisfaction at Time 2. The study underscores the importance of promoting moderate-to-vigorous physical activity, particularly on weekends and holidays, to improve mental health outcomes among adolescents.

Examining the effects of prekindergarten to second grade classroom quality on early elementary achievement

Previous research has investigated the influence of preschool interventions in early elementary environments with some results suggesting that high quality early preschool experiences may have meaningful effects on student academic outcomes not only immediately following the preschool years but also in the long term. This study aimed to investigate this topic further by using a novel cross-classified model to account for the complex hierarchical structure of a large-scale longitudinal and multilevel dataset. First, we examined the carryover classroom effects from prekindergarten (PK), kindergarten (K), and first grade on later grades’ reading and mathematics scores. Second, we explored the effect of PK to second grade classroom quality on students’ K, first, and second grade reading and mathematics scores. Our analyses yielded significant PK carryover classroom effects for reading; these results provide evidence that a student’s PK classroom continues to impact that student for at least three years after. Additionally, one domain of PK classroom quality —classroom organization—was found to be positively related to students’ K, first, and second grade reading outcomes.

3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs.

Self-supervised learning (SSL) has emerged as a powerful technique for improving the efficiency and effectiveness of deep learning models. Contrastive methods are a prominent family of SSL that extract similar representations of two augmented views of an image while pushing away others in the representation space as negatives. However, the state-of-the-art contrastive methods require large batch sizes and augmentations designed for natural images that are impractical for 3D medical images. To address these limitations, we propose a new longitudinal SSL method, 3DTINC, based on non-contrastive learning. It is designed to learn perturbation-invariant features for 3D optical coherence tomography (OCT) volumes, using augmentations specifically designed for OCT. We introduce a new non-contrastive similarity loss term that learns temporal information implicitly from intra-patient scans acquired at different times. Our experiments show that this temporal information is crucial for predicting progression of retinal diseases, such as age-related macular degeneration (AMD). After pretraining with 3DTINC, we evaluated the learned representations and the prognostic models on two large-scale longitudinal datasets of retinal OCTs where we predict the conversion to wet-AMD within a six-month interval. Our results demonstrate that each component of our contributions is crucial for learning meaningful representations useful in predicting disease progression from longitudinal volumetric scans.

The Wellcome Trust: new funding for mental health science.

The Wellcome Trust: new funding for mental health science.

Physician engagement in online medical teams: A multilevel investigation

Given the widespread adoption of artificial intelligence technologies in the healthcare industry, efficient healthcare delivery has become increasingly important. In creating a patient-centered and engaging technology, the omnichannel healthcare model has expanded to allow consumers access to online health communities, including physician-led online medical teams (OMTs). To help ensure the continued growth of this emerging omnichannel touchpoint, we develop a three-level theoretical model using a combination of hierarchical linear modeling and panel data identification to analyze a large-scale longitudinal dataset consisting of 2,185 physicians across 1,400 OMTs. In line with self-determination theory, we identify physician-level (i.e., intrinsic and extrinsic) and team-level (i.e., social interaction ties) motivational drivers of physician engagement, along with managerially relevant boundary conditions. Based on our findings, we suggest a multi-level strategy for ensuring the continued growth and success of OMTs in facilitating value co-creation between health providers and patients.

Establishment of the large-scale longitudinal multi-omics dataset in COVID-19 patients: data profile and biospecimen

Understanding and monitoring virus-mediated infections has gained importance since the global outbreak of the coronavirus disease 2019 (COVID-19) pandemic. Studies of high-throughput omics-based immune profiling of COVID-19 patients can help manage the current pandemic and future virus-mediated pandemics. Although COVID-19 is being studied since past 2 years, detailed mechanisms of the initial induction of dynamic immune responses or the molecular mechanisms that characterize disease progression remains unclear. This study involved comprehensively collected biospecimens and longitudinal multi-omics data of 300 COVID-19 patients and 120 healthy controls, including whole genome sequencing (WGS), single-cell RNA sequencing combined with T cell receptor (TCR) and B cell receptor (BCR) sequencing (scRNA(+scTCR/BCR)-seq), bulk BCR and TCR sequencing (bulk TCR/BCR-seq), and cytokine profiling. Clinical data were also collected from hospitalized COVID-19 patients, and HLA typing, laboratory characteristics, and COVID-19 viral genome sequencing were performed during the initial diagnosis. The entire set of biospecimens and multi-omics data generated in this project can be accessed by researchers from the National Biobank of Korea with prior approval. This distribution of large-scale multi-omics data of COVID-19 patients can facilitate the understanding of biological crosstalk involved in COVID-19 infection and contribute to the development of potential methodologies for its diagnosis and treatment.

A closer look at the domain-specific associations of openness with language achievement: Evidence on the role of intrinsic value from two large-scale longitudinal studies.

Many empirical investigations focus on how personality traits and academic motivation are related to academic achievement. Regarding the personality traits described in the five-factor model, prior research has shown associations between openness to experience and language achievement in particular. Following the principle of trait activation, associations with intrinsic value can explain these domain-specific relationships of openness with achievement. This study investigated whether intrinsic value mediates the associations of openness to experience with language achievement to understand these associations more closely. Based on the principle of trait activation, we combined openness to experience with intrinsic value as a motivational construct more proximal to behaviour. We capitalized on two large-scale longitudinal data sets (N=1994; M=16.8 years; 51% female; and N=2722; M=17.34 years; 58% female) of German-speaking students in upper secondary education with two points of measurement, respectively. We assessed achievement using report card grades and standardized test scores, capturing three language subskills: reading and listening, measured by standardized tests, and writing competencies, measured by essay assignments. We conducted mediation analysis using bootstrapped confidence intervals and robust standard errors to account for dependencies in the data due to students clustered within classrooms. Overall, our results show that intrinsic value mediated the relationship between openness and achievement in English as a foreign language. The results remained stable when controlling for prior achievement, predicting achievement gains. Our investigation provides empirical insights into the manifestations of personality in academic contexts. We discuss the limitations of our approach, with a focus on the use of a mediation analysis in this research design.

TR-GAN: Multi-Session Future MRI Prediction With Temporal Recurrent Generative Adversarial Network

Magnetic Resonance Imaging (MRI) has been proven to be an efficient way to diagnose Alzheimer's disease (AD). Recent dramatic progress on deep learning greatly promotes the MRI analysis based on data-driven CNN methods using a large-scale longitudinal MRI dataset. However, most of the existing MRI datasets are fragmented due to unexpected quits of volunteers. To tackle this problem, we propose a novel Temporal Recurrent Generative Adversarial Network (TR-GAN) to complete missing sessions of MRI datasets. Unlike existing GAN-based methods, which either fail to generate future sessions or only generate fixed-length sessions, TR-GAN takes all past sessions to recurrently and smoothly generate future ones with variant length. Specifically, TR-GAN adopts recurrent connection to deal with variant input sequence length and flexibly generate future variant sessions. Besides, we also design a multiple scale & location (MSL) module and a SWAP module to encourage the model to better focus on detailed information, which helps to generate high-quality MRI data. Compared with other popular GAN architectures, TR-GAN achieved the best performance in all evaluation metrics of two datasets. After expanding the Whole MRI dataset, the balanced accuracy of AD vs. cognitively normal (CN) vs. mild cognitive impairment (MCI) and stable MCI vs. progressive MCI classification can be increased by 3.61% and 4.00%, respectively.

Approximating the Manifold Structure of Attributed Incentive Salience from Large-scale Behavioural Data

Incentive salience attribution can be understood as a psychobiological mechanism ascribing relevance to potentially rewarding objects and actions. Despite being an important component of the motivational process guiding our everyday behaviour its study in naturalistic contexts is not straightforward. Here we propose a methodology based on artificial neural networks (ANNs) for approximating latent states produced by this process in situations where large volumes of behavioural data are available but no experimental control is possible. Leveraging knowledge derived from theoretical and computational accounts of incentive salience attribution we designed an ANN for estimating duration and intensity of future interactions between individuals and a series of video games in a large-scale (N > 3 × 106) longitudinal dataset. We found video games to be the ideal context for developing such methodology due to their reliance on reward mechanics and their ability to provide ecologically robust behavioural measures at scale. When compared to competing approaches our methodology produces representations that are better suited for predicting the intensity future behaviour and approximating some functional properties of attributed incentive salience. We discuss our findings with reference to the adopted theoretical and computational frameworks and suggest how our methodology could be an initial step for estimating attributed incentive salience in large-scale behavioural studies.

Educationally maintained inequality? The role of risk factors and resilience at 9, 13 and 17 in disabled young people’s post-school pathways at 20

ABSTRACT While Ireland has the highest level of participation in higher education (HE) in Europe, it also has one of the widest participation gaps between disabled and non-disabled young people. Using a large-scale longitudinal dataset, we assess how disabled young people compare with non-disabled peers in accessing a range of post-school educational pathways. Extending the effectively maintained inequality perspective to disability research, our results highlight important qualitative differences in disabled people’s trajectories. Ultimately, we find greater progression to Further Education and Training, and lower participation in Level 8 degrees in universities, all else being equal. Entry to HE is lower among those with socio-emotional difficulties, even after controlling for key characteristics, while attendance among young people with an intellectual disability or specific learning difficulty is mediated by academic performance. The results highlight the importance of school attendance, engagement and achievement during the primary and early secondary years, and parental expectations, raising important implications for inclusive education. The multiple challenges facing disabled young people in Ireland are evident, reflecting the direct and indirect impact of socio-economic disadvantage, at family, school and community levels. Stronger linkages across the tertiary landscape and stronger HE transition supports, particularly for young adults with socio-emotional/psychological difficulties, are needed.

Economic impact of road transportation infrastructure projects: the case of Egnatia Odos Motorway

ABSTRACT Non-cyclical and high-leverage infrastructure projects, such as road transportation networks, have been considered as critical policy instruments to promote growth and social development. Yet, their growth-generating effect on the economic welfare of countries and regions has come under scrutiny in recent years: several studies have nuanced the implicit positive associations between investments in transportation and economic benefits. Building upon a set of traffic flow characteristics as potential correlates to the regional development indicator, this research focuses on the economic impacts of Egnatia Odos Motorway (EOM) on Greece's Northern region. To specify, by employing a large-scale longitudinal dataset which includes over 230 million entry records of various vehicle types along EOM, generated from toll collection systems between 2010 and 2019, we investigate the associations between traffic data and potential variations in regional GDP per capita annual growth rate. Our results suggest positive associations between regional economic development and the EOM operation and, more specifically, with passengers' transportation and inland freight. We conclude that transportation big data provide essential input for the appraisal of a road transport investment project, reveal the status of regional welfare, and may contain valuable information for spatial management and planning.

A compilation of fecal microbiome shotgun metagenomics from hematopoietic cell transplantation patients

Hospitalized patients receiving hematopoietic cell transplants provide a unique opportunity to study the human gut microbiome. We previously compiled a large-scale longitudinal dataset of fecal microbiota and associated metadata, but we had limited that analysis to taxonomic composition of bacteria from 16S rRNA gene sequencing. Here we augment those data with shotgun metagenomics. The compilation amounts to a nested subset of 395 samples compiled from different studies at Memorial Sloan Kettering. Shotgun metagenomics describes the microbiome at the functional level, particularly in antimicrobial resistances and virulence factors. We provide accession numbers that link each sample to the paired-end sequencing files deposited in a public repository, which can be directly accessed by the online services of PATRIC to be analyzed without the users having to download or transfer the files. Then, we show how shotgun sequencing enables the assembly of genomes from metagenomic data. The new data, combined with the metadata published previously, enables new functional studies of the microbiomes of patients with cancer receiving bone marrow transplantation.

Ability-related political polarization in the COVID-19 pandemic

In two large-scale longitudinal datasets (combined N = 5761), we investigated ability-related political polarization in responses to the COVID-19 pandemic. We observed more polarization with greater ability in emotional responses, risk perceptions, and product-purchase intentions across five waves of data collection with a diverse, convenience sample from February 2020 through July 2020 (Study 1, N = 1267). Specifically, more liberal participants had more negative emotional responses and greater risk perceptions of COVID-19 than conservative participants. Compared to conservatives, liberal participants also interpreted quantitative information as indicating higher COVID-19 risk and sought COVID-related news more from liberal than conservative news media. Of key importance, we also compared verbal and numeric cognitive abilities for their independent capacity to predict greater polarization. Although measures of numeric ability, such as objective numeracy, are often used to index ability-related polarization, ideological differences were more pronounced among those higher in verbal ability specifically. Similar results emerged in secondary analysis of risk perceptions in a nationally representative longitudinal dataset (Study 2, N = 4494; emotions and purchase intentions were not included in this dataset). We further confirmed verbal-ability-related polarization findings on non-COVID policy attitudes (i.e., weapons bans and Medicare-for-all) measured cross-sectionally. The present Study 2 documented ability-related polarization emerging over time for the first time (rather than simply measuring polarization in existing beliefs). Both studies demonstrated verbal ability measures as the most robust predictors of ability-related polarization. Together, these results suggest that polarization may be a function of the amount and/or application of verbal knowledge rather than selective application of quantitative reasoning skills.

GazeBase, a large-scale, multi-stimulus, longitudinal eye movement dataset

This manuscript presents GazeBase, a large-scale longitudinal dataset containing 12,334 monocular eye-movement recordings captured from 322 college-aged participants. Participants completed a battery of seven tasks in two contiguous sessions during each round of recording, including a – (1) fixation task, (2) horizontal saccade task, (3) random oblique saccade task, (4) reading task, (5/6) free viewing of cinematic video task, and (7) gaze-driven gaming task. Nine rounds of recording were conducted over a 37 month period, with participants in each subsequent round recruited exclusively from prior rounds. All data was collected using an EyeLink 1000 eye tracker at a 1,000 Hz sampling rate, with a calibration and validation protocol performed before each task to ensure data quality. Due to its large number of participants and longitudinal nature, GazeBase is well suited for exploring research hypotheses in eye movement biometrics, along with other applications applying machine learning to eye movement signal analysis. Classification labels produced by the instrument’s real-time parser are provided for a subset of GazeBase, along with pupil area.

Valuing supply chain performance within and across manufacturing industries: A DEA-based approach

Managers often use financial metrics based on internal accounting data to gauge firm performance. In this paper, we analyze firm value and related levers of operational supply chain (SC) performance from a financial market perspective. This allows us to study the contributions of profitability (earns) and asset utilization (turns) as the two major drivers of firm value. For this purpose, we apply data envelopment analysis to a large-scale longitudinal dataset of listed US companies (2007–2015) that covers 13 manufacturing industries. In so doing, we shed light on the implications of the 2008/2009 financial crisis for operational SC performance. Our findings can be summarized as follows: First, earns and turns are negatively correlated from an internal accounting-based viewpoint that distinguishes ‘high earns/low turns’ from ‘low earns/high turns’ industries. Second, manufacturing companies mostly tend to be less efficient in translating earns as opposed to turns into firm value. Finally, we observe declining efficacy of approaches to value-based supply chain management in manufacturing industries since 2007 (the last year before the financial crisis). General firm value appreciation in the stock markets overcompensated for this decline until 2015.

Functional Connectivity Predicts Individual Development of Inhibitory Control during Adolescence.

Derailment of inhibitory control (IC) underlies numerous psychiatric and behavioral disorders, many of which emerge during adolescence. Identifying reliable predictive biomarkers that place the adolescents at elevated risk for future IC deficits can help guide early interventions, yet the scarcity of longitudinal research has hindered the progress. Here, using a large-scale longitudinal dataset in which the same subjects performed a stop signal task during functional magnetic resonance imaging at ages 14 and 19, we tracked their IC development individually and tried to find the brain features predicting their development by constructing prediction models using 14-year-olds' functional connections within a network or between a pair of networks. The participants had distinct between-subject trajectories in their IC development. Of the candidate connections used for prediction, ventral attention-subcortical network interconnections could predict the individual development of IC and formed a prediction model that generalized to previously unseen individuals. Furthermore, we found that connectivity between these two networks was related to substance abuse problems, an IC-deficit related problematic behavior, within 5years. Our study reveals individual differences in IC development from mid- to late-adolescence and highlights the importance of ventral attention-subcortical network interconnections in predicting future IC development and substance abuse in adolescents.

Speaking and Reading in Two Languages: On the Identification of Reading and Language Disabilities in Spanish-Speaking English Learners.

Articles in this issue examine (1) the primary sources of variability in reading and language achievement among Spanish-speaking English learners (ELs) in the United States, (2) the extent to which poor performance at the end of grade 2 is identifiable in developmental trajectories beginning in kindergarten, (3) the relations among core reading constructs of phonological awareness and decoding in both English and Spanish and the factors that affect their relationship, (4) the performance of different approaches to identification and the factors that influence how well they work, as well as (5) the growing literature focused on intervention for reading problems in this population. This article examines the literature on language minority students and disability identification and analyzes a large-scale longitudinal dataset (>4,000 ELs; >15,000 observations) to systematically characterize and describe the oral language and reading development of Spanish-speaking children designated as ELs from kindergarten to second grade, considering a range of factors that may potentially contribute to that characterization and its relation to academic performance. This systematic characterization should facilitate the development of an empirical basis for a theoretically grounded framework of typical development in ELs in order to more precisely identify those children with language and learning disabilities.

Language as a bridge to higher education: a large-scale empirical study of heritage language proficiency on language minority students’ academic success

ABSTRACTIn a nation where monolingualism predominates, heritage language (HL) has been downplayed as less important in educational contexts in the U.S. Disproportionately little attention has been given to the role of HL in language minority (LM) students’ attendance to postsecondary education (PSE) (cf. Brutt-Griffler, Janina, and Manka Varghese, eds. 2004. Bilingualism and Language Pedagogy. Clevedon: Multilingual Matters Press). To address this void, this study investigates whether LM high school students’ HL proficiency and use have influence on high school completion and college attendance. To further identify whether these relationships differ by cross-linguistic similarities between HL and English, comparisons are made between Asian and Latinx LM students. We analyse a nationally representative large-scale longitudinal dataset (NELS) using the programming language R. The results from logistic regressions underscore the distinctive role of HL on LM students’ high school completion and differentiated effects of HL on PSE pathways. Among our findings, we show that for Latinx students, HL literacy proficiency was a positive predictor of their four-year college attendance; whereas, for Asian students, HL use at home was a positive predictor. The positive effects were observed after controlling for their individual and family background. The study supports the argument that continued use of HL and HL literacy hold the key to enhancing academic attainment.

Families' Division of Labor and Social Networks in the 21st Century: Revisiting Elizabeth Bott's Classic Hypotheses.

In 1957, Elizabeth Bott argued that the organization of family and social networks are intertwined and that the structure and composition of social networks are associated with the ways in which spouses divide household and paid labor. While this idea became a classic in the literature addressing the division of labor, societies have changed tremendously in the past 50 years, and it has become far more common for spouses to divide their labor more equally. In addition, the causal direction is not clear: Do networks affect the division of labor or vice versa? We inquired as to the causal relationship using a large-scale longitudinal data set, collected in 2009/2010 and 2011/2012 (n = 2477; PAIRFAM [Panel Analysis of Intimate Relationships and Family Dynamics]). We found moderate support for the hypothesis that personal networks influence the division of labor in households, but there were stronger effects for the reverse—that is, that the division of labor affects network patterns, particularly for women.

Ethnicity and Neighborhood Attainment in England and Wales

Ethnic minorities’ spatial concentration and their predominance in deprived areas are two well-known patterns that characterize Britain’s social landscape. However, little is known about ethnic minorities’ opportunities for spatial integration, especially after individual, social origin and childhood neighborhood characteristics have been taken into consideration. Using a large-scale longitudinal dataset of England and Wales covering a forty-year period (1971-2011), in combination with aggregated Census data, the author examines ethnic inequalities in access to neighborhoods with varying levels of ethnic concentration and deprivation. The article reveals that ethnic minorities are less likely than white British individuals to reside in ‘whiter’ and less deprived neighborhoods. These effects, however, reduce, for most groups, among those with higher education and a higher social class, in line with one version of the place stratification/ethnic enclave model. Growing up in areas with high ethnic concentration and high deprivation has a particularly strong ‘retention effect’ among Asians: their probabilities of being found in ‘whiter’ and less deprived areas in adulthood are the lowest. Among Africans, growing up in a less deprived area brings no benefits for future neighborhood outcomes. Ethnic groups’ unequal access to neighborhoods suggests that group-specific preferences and constrains play a role in neighborhood attainment.