Mohammadzadeh Gonabadi Arash, Fallahtafti Farahnaz, Heselton Judith, Myers Sara A, Siu Ka-Chun, Boron Julie Blaskewicz
Institute for Rehabilitation Science and Engineering, Madonna Rehabilitation Hospitals, Lincoln, NE 68506, USA.
Department of Biomechanics and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, NE 68182, USA.
Biomimetics (Basel). 2025 May 29;10(6):351. doi: 10.3390/biomimetics10060351.
Dual-task paradigms that combine cognitive and motor tasks offer a valuable lens for detecting subtle impairments in cognitive and physical functioning, especially in older adults. This study used artificial neural network (ANN) modeling to predict clinical, cognitive, and psychosocial outcomes from integrated gait, speech-linguistic, demographic, physiological, and psychological data collected during single- and dual-task conditions. Forty healthy adults (ages 20-84) completed physical, cognitive, and psychosocial assessments and a dual-task walking task involving cell phone use. ANN models were optimized using hyperparameter tuning and k-fold cross-validation to predict outcomes such as the Montreal Cognitive Assessment (MOCA), Trail Making Tests (TMT A and B), Activities-Specific Balance Confidence (ABC) Scale, Geriatric Depression Scale (GDS), and measures of memory, affect, and social support. The models achieved high accuracy for MOCA (100%), ABC (80%), memory function (80%), and social support satisfaction (75%). Feature importance analyses revealed key predictors such as speech-linguistic markers and sensory impairments. First-person plural pronoun used and authenticity of internal thoughts during dual-task emerged as strong predictors of MOCA and memory. Models were less accurate for complex executive tasks like TMT A and B. These findings support the potential of ANN models for the early detection of cognitive and psychosocial changes.
将认知任务和运动任务相结合的双重任务范式为检测认知和身体功能的细微损伤提供了一个有价值的视角，尤其是在老年人中。本研究使用人工神经网络（ANN）建模，根据在单任务和双任务条件下收集的综合步态、言语语言、人口统计学、生理学和心理学数据，预测临床、认知和心理社会结果。40名健康成年人（年龄在20 - 84岁之间）完成了身体、认知和心理社会评估以及一项涉及使用手机的双任务步行任务。使用超参数调整和k折交叉验证对ANN模型进行优化，以预测诸如蒙特利尔认知评估（MOCA）、连线测验（TMT A和B）、特定活动平衡信心（ABC）量表、老年抑郁量表（GDS）以及记忆、情感和社会支持等指标。这些模型在MOCA（100%）、ABC（80%）、记忆功能（80%）和社会支持满意度（75%）方面取得了较高的准确率。特征重要性分析揭示了关键预测因素，如言语语言标记和感觉障碍。双任务期间使用的第一人称复数代词和内心想法的真实性成为MOCA和记忆的有力预测因素。对于像TMT A和B这样的复杂执行任务，模型的准确率较低。这些发现支持了ANN模型在早期检测认知和心理社会变化方面的潜力。
