用可穿戴运动数据训练出能预测心理健康的通用模型。
A Foundation Model for Wearable Movement Data in Mental Health Research
- 用自监督掩码自动编码器预训练时序动作数据，构建时间建模能力。
- 在21,538人数据上训练，对药物使用等心理状态预测准确率显著提升。
- 提供可解释注意力图，帮助医生理解关键行为模式，适合临床研究者使用。
可穿戴设备收集的运动数据几乎涵盖所有主流智能手表，是精神健康研究中反映精细时间行为趋势的宝贵资源。尽管前景广阔，健康可穿戴建模的基础模型发展仍远落后于临床图像与文本分析。本文设计了基于片段嵌入的Transformer，并在分钟级、周长的动作记录（actigraphy）序列上采用自监督掩码自动编码器进行预训练，开发并评估了预训练动作记录变压器（PAT）。PAT是一个开源的可穿戴运动时间序列基础模型，融合了周级时间建模、精神健康结果评估与公共数据上的可复现性。其在来自美国国家健康与营养调查（NHANES）的21,538名全国代表性参与者数据上预训练，各项心理健康预测任务表现均优于非基础模型基线——包括苯二氮䓬类和SSRI用药、抑郁症状及睡眠异常。在苯二氮䓬用药预测任务中，相比常用时序模型，PAT分别提升55.6%（对比LSTM）、21.4%（对比1-D CNN）、14.8%（对比ConvLSTM）。除预测精度外，PAT还提供可解释的注意力图，揭示对临床预测最关键的日常活动时段，增强模型透明度并带来潜在临床洞见。结果表明，PAT为研究人员与临床医生提供了易部署、可适配、可扩展的解决方案，推动从可穿戴传感器数据中获取临床洞察。
原文摘要 · Abstract (English)
Wearable movement data is collected by nearly all commercially available smartwatches and is a valuable resource for mental health research, reflecting fine-grained temporal behavioral trends. Despite its promise, the development of foundation models for health wearable modeling remains limited when compared to clinical image and text analysis. We designed transformers with patch embeddings and used self-supervised masked autoencoder pretraining on minute-level week-long actigraphy (physical activity intensity measurement) sequences to develop and evaluate the Pretrained Actigraphy Transformer (PAT). PAT is an open-source foundation model for wearable movement time series that combines week-long temporal modeling, psychiatric outcome evaluation, and reproducibility on public data. Pretrained on data from 21,538 U.S. participants in a nationally representative cohort from the National Health and Nutrition Examination Survey (NHANES), PAT consistently outperformed non-foundation-model baselines across mental health prediction tasks-including benzodiazepine and SSRI use, depression, and sleep abnormalities. During the benzodiazepine medication usage prediction task, PAT demonstrated the largest improvement over non-foundational deep learning models commonly used for time-series modeling (i.e., 55.6% improvement over the LSTM, 21.4% improvement over the 1-D CNN, 14.8% improvement over the ConvLSTM). Beyond predictive accuracy, PAT provides interpretable attention maps highlighting specific periods of daily activity most important for clinical predictions, offering model transparency and potential clinical insights. The results suggest that PAT offers an easy-to-deploy, adaptable and scalable solution to advance clinical insight from wearable sensor data for researchers and clinicians. GitHub: https://github.com/njacobsonlab/Pretrained-Actigraphy-Transformer/
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