A novel dual-task paradigm with story recall shows significant differences in the gait kinematics in older adults with cognitive impairment: A cross-sectional study

Abstract

ObjectiveCognitive and motor dysfunctions in older people become more evident while dual-tasking. Several dual-task paradigms have been used to identify older individuals at the risk of developing Alzheimer’s disease and dementia. This study evaluated gait kinematic parameters for dual-task (DT) conditions in older adults with mild cognitive impairment (MCI), subjective cognitive decline (SCD), and normal cognition (NC).MethodThis is a cross-sectional, clinical-based study carried out at the Zhongshan Rehabilitation Branch of First Affiliated Hospital of Nanjing Medical University, China.ParticipantsWe recruited 83 community-dwelling participants and sorted them into MCI (n = 24), SCD (n = 33), and NC (n = 26) groups based on neuropsychological tests. Their mean age was 72.0 (5.55) years, and male–female ratio was 42/41 (p = 0.112). Each participant performed one single-task walk and four DT walks: DT calculation with subtracting serial sevens; DT naming animals; DT story recall; and DT words recall.Outcome and measuresKinematic gait parameters of speed, knee peak extension angle, and dual-task cost (DTC) were obtained using the Vicon Nexus motion capture system and calculated by Visual 3D software. A mixed-effect linear regression model was used to analyze the data.ResultsThe difference in gait speed under DT story recall and DT calculation was −0.099 m/s and − 0.119 m/s (p = 0.04, p = 0.013) between MCI and SCD, respectively. Knee peak extension angle under DT story recall, words recall, and single task was bigger in the MCI group compared to the NC group, respectively (p = 0.001, p = 0.001, p = 0.004). DTC was higher in the DT story recall test than all other DT conditions (p < 0.001).ConclusionKinematic gait parameters of knee peak extension angle for the DT story recall were found to be sensitive enough to discriminate MCI individuals from NC group. DTC under DT story recall was higher than the other DT conditions.

Entropy-Based Machine Learning Model for Fast Diagnosis and Monitoring of Parkinson’s Disease

“…Presently, some methods are available for detecting neurodegenerative diseases. Using data collected from question assessments [13], blood biomarkers [14], eye-tracking parameters [15], kinematic gait parameters [16], electroencephalograms (EEG) [17], and other tests, conclusions are drawn regarding the presence or high risk of developing PD.…”

“…Generally, five different frequency bands are investigated in EEG signals depending on their application. These bands are delta (0-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49) [22].…”

Detection of Mild Cognitive Impairment Through Hand Motor Function Under Digital Cognitive Test: Mixed Methods Study

“…Their findings revealed that a classifier based on dual-task gait features could detect older adults who scored low on the Mini-Mental State Examination (MMSE) test [ 20 ]. In addition, Ali et al [ 21 ] used the Vicon Nexus 2.8 motion capture system to obtain kinematic gait parameters such as velocity, peak knee extension angle, and dual-task cost for participants. Their study showed that the kinematic gait parameters of the dual-task peak knee extension angle for story recall gait could effectively differentiate the MCI group from the healthy control (HC) group.…”