TY - JOUR
AU - Mohammadzadeh Gonabadi
AU - F. Fallahtafti
AU - J. Heselton
AU - S. A. Myers
AU - K. C. Siu
AU - J. B. Boron
A1 - 
AB - 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.
AD - 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.; Department of Gerontology, University of Nebraska at Omaha, Omaha, NE 68182, USA.; Department of Surgery and Research Service, Nebraska-Western Iowa Veterans Affairs Medical Center, Omaha, NE 68105, USA.; Department of Health and Rehabilitation Sciences, University of Nebraska Medical Center, Omaha, NE 68198, USA.
AN - 40558320
BT - Biomimetics (Basel)
C5 - Healthcare Disparities
CP - 6
DA - May 29
DO - 10.3390/biomimetics10060351
DP - NLM
ET - 20250529
IS - 6
JF - Biomimetics (Basel)
LA - eng
N2 - 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.
PY - 2025
SN - 2313-7673
ST - Modeling Dual-Task Performance: Identifying Key Predictors Using Artificial Neural Networks
T1 - Modeling Dual-Task Performance: Identifying Key Predictors Using Artificial Neural Networks
T2 - Biomimetics (Basel)
TI - Modeling Dual-Task Performance: Identifying Key Predictors Using Artificial Neural Networks
U1 - Healthcare Disparities
U3 - 10.3390/biomimetics10060351
VL - 10
VO - 2313-7673
Y1 - 2025
ER -