发表机构
The University of Queensland; Mawlana Bhashani Science and Technology University（昆士兰大学; 毛拉纳·巴沙尼科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对青少年晶体智力预测，提出SATURN时间图回归网络，利用21天活动与睡眠数据，通过动态剪枝和注意力池化，达到R²=0.2783，优于基线，并识别关键预测因子。
AI 中文摘要
可穿戴活动记录提供了一种可扩展、生态效度高的替代方案，用于替代 episodic 临床评估。然而，从这些轨迹中预测青少年晶体智力（$G_c$）的连续值仍具挑战性，原因在于设备依从性不规则以及复杂的行为-环境交互。我们利用来自青少年大脑认知发展研究（版本5.1）中6,091名青少年的21天Fitbit记录生成的每日汇总数据来解决此问题。我们提出SATURN，一种睡眠-活动时间统一回归网络。它将参与者表示为21节点的时间图，编码每日行为和时间邻接关系。为防止插补伪影，在前向传播过程中动态剪枝无效日边。节点嵌入通过残差GATv2层细化，经掩码注意力池化聚合，并与社会人口统计学协变量融合。在家庭控制、年龄-性别-BMI分层交叉验证下，SATURN达到$R^2 = 0.2783 \u00b1 0.0127$，持续优于扁平化机器学习（梯度提升，$R^2 = 0.2372$）和序列深度学习（BiLSTM，$R^2 = 0.2688$）基线。可解释性分析识别出轻度活动、代谢当量和睡眠时长为主要预测因子，而蒙特卡洛dropout和亚组分析确认了跨社会人口统计学层的公平性能。最终，SATURN为数字认知表型分析建立了严格的计算框架，通过突出宏观行为异常，提供了一种可扩展的途径来补充传统评估。
英文摘要
Wearable actigraphy offers a scalable, ecologically valid alternative to episodic clinical assessment. However, predicting continuous adolescent crystallized intelligence ($G_c$) from such traces remains challenging due to irregular device adherence and complex behavioral-environmental interactions. We address this using daily summary data derived from 21-day Fitbit records of 6,091 adolescents in the Adolescent Brain Cognitive Development Study (Release 5.1). We propose SATURN, a Sleep-Activity Temporal Unified Regression Network. It represents participants as 21-node temporal graphs encoding daily behaviors and temporal adjacency. To prevent imputation artifacts, invalid-day edges are dynamically pruned during forward passes. Node embeddings are refined via residual GATv2 layers, aggregated through masked attention pooling, and fused with sociodemographic covariates. Under family-controlled, age-sex-BMI-stratified cross-validation, SATURN achieves $R^2 = 0.2783 \pm 0.0127$, consistently improving upon flattened machine learning (Gradient Boosting, $R^2 = 0.2372$) and sequential deep learning (BiLSTM, $R^2 = 0.2688$) baselines. Explainability analyses identify light activity, metabolic equivalents, and sleep duration as dominant predictors, while Monte Carlo dropout and subgroup analyses confirm equitable performance across sociodemographic strata. Ultimately, SATURN establishes a rigorous computational framework for digital cognitive phenotyping, offering a scalable pathway to complement traditional assessments by highlighting macro-level behavioral anomalies.
Comments11 pages, 3 figures. This work has been submitted to the IEEE Transactions on Computational Social Systems for possible publication
