Search arXiv⌕ Search

arXiv · 2609.32982

Protocol for an Observational Study on the Effects of Adolescent Physical Activity and Sports Participation on Flourishing and Academic Engagement

Abstract

The wide-ranging benefits of physical activity and sports participation among children and adolescents have been closely examined and are reported to include improvements in physical and mental health, cognitive functioning, and social connectedness. However, it remains largely unknown how these activities may affect flourishing and academic engagement, which are closely tied to long-term success, health, and well-being, and how these patterns evolved before, during, and after the COVID-19 pandemic. In this article, we provide the protocol for an observational study using data from the National Survey of Children's Health to examine these relationships among American adolescents. To strengthen our findings, we will conduct this investigation across three different time periods, allowing us to assess the replicability of our conclusions. We introduce a novel statistical design, called data turnover, to carry out this analysis. Data turnover allows a single group of statisticians and domain experts to work together to assess the strength of evidence gathered across multiple data splits while incorporating both qualitative and quantitative findings from data exploration. We delineate our analysis plan using this new method and conclude with a brief discussion of additional considerations for our study.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

William Bekerman, Rebecca E. Hasson, Leah E. Robinson, Dylan S. Small. 2026-09-26. Protocol for an Observational Study on the Effects of Adolescent Physical Activity and Sports Participation on Flourishing and Academic Engagement. https://arxiv.org/abs/2609.32982

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Steer-to-Detect: Probing Hidden Representations for Detection of LLM-Generated Texts

The rapid advancement of large language models (LLMs) has made machine-generated text increasingly difficult to distinguish from human-written text. While recent studies explore leveraging internal representations of language models to uncover deeper detection signals, these raw features often exhibit substantial overlap between classes, limiting their discriminative power. To address this challenge, we propose Steer-to-Detect (\texttt{S2D}), a two-stage framework for detecting LLM-generated text. In the first stage, \texttt{S2D} learns a steering vector that is injected into the hidden states of a frozen observer LLM, producing representations with improved class separability. In the second stage, detection is performed via a hypothesis testing procedure based on the steered representations. We establish finite-sample, high-probability guarantees for Type I and Type II errors, providing a theoretical characterization of the procedure. Empirically, \texttt{S2D} achieves strong and consistent performance across a range of settings, including out-of-distribution scenarios and adversarial perturbations.

stat.AP↗

Multilayer-Dynamic Network Clustering with Application to World Trade Data

International trade data, such as the FAO dataset, can be naturally represented as \emph{multilayer-dynamic networks}, where countries are nodes, trade relationships are edges, different products correspond to different layers, and networks evolve over time. An important problem is how to identify evolving community structures in such multilayer-dynamic trade networks. Motivated by this problem, we study community detection in multilayer-dynamic networks, allowing the community structure to vary across both layers and time. We propose a novel method, \emph{MuDySC} (Multilayer-Dynamic Spectral Clustering), which smooths the eigenspace projection matrices across adjacent time points and across layers at the same time point. We develop an efficient alternating iterative algorithm and establish both global and local convergence results, with the latter allowing weaker conditions on the tuning parameters when the relevant eigenspaces are sufficiently close and the algorithm is suitably initialized. We apply MuDySC to the FAO data. The analysis reveals clear asymmetry between export and import community structures and highlights both persistent and shifting trade positions of major countries. As an extension to accommodate substantial heterogeneity across network layers, we develop TLC-MuDySC, a tensor-based layer-clustering method that first identifies structurally related layers and then applies MuDySC within the estimated layer groups.

stat.AP↗

Mental Health Support Hotlines as Capacity-Limited Services: Joint Modeling of Demand, Assessment, and Unassessed Calls

Mental health support hotlines log help-seeking call attempts in real time, yet limited service capacity allows only a small proportion of calls to be answered and assessed. The time of every attempt is recorded, whereas issue types and marks such as caller demographics and crisis severity are observed only for assessed calls. Caller anonymity also prevents linking repeated attempts to the same individual. Nevertheless, service planning and crisis monitoring require estimating unassessed demand, recovering temporal patterns in call content, and quantifying how much the partially observed marks improve these estimates. We develop a Joint Marked Dynamic Factor Model (JM-DFM) in which a shared low-dimensional latent state drives attempt intensity, issue composition, and mixed-type mark distributions. We establish identifiability, prove consistency of the proposed sieve estimator, and show that incorporating marks improves the asymptotic estimation precision. The proposed framework is then applied to records of 68,371 call attempts to China's national 12356 mental health support hotline. We estimate that about 35 unassessed attempts per day involve suicidal ideation, representing a higher proportion than among assessed calls. Incorporating the marks narrows the latent-state uncertainty bands by 18% to 49%, most during periods with few issue-labeled calls.

stat.AP↗