The association between cognition and dual-tasking among older adults: the effect of motor function type and cognition task difficulty
Bibliographic record
Abstract
Hossein Ehsani,1,2 Martha Jane Mohler,1–3 Kathy O’Connor,4 Edward Zamrini,4,5 Coco Tirambulo,2 Nima Toosizadeh1–3 1Department of Biomedical Engineering, University of Arizona, Tucson, AZ, USA; 2Arizona Center on Aging, Department of Medicine, University of Arizona, Tucson, AZ, USA; 3Division of Geriatrics, General Internal Medicine and Palliative Medicine, Department of Medicine, University of Arizona, Tucson, AZ, USA; 4Neurology Department, Banner Sun Health Research Institute, Sun City, AZ, USA; 5Department of Neurology, University of Utah, Salt Lake City, UT, USA Background: Dual-task actions challenge cognitive processing. The usefulness of objective methods based on dual-task actions to identify the cognitive status of older adults has been previously demonstrated. However, the properties of select motor and cognitive tasks are still debatable. We investigated the effect of cognitive task difficulty and motor task type (walking versus an upper-extremity function [UEF]) in identifying cognitive impairment in older adults. Methods: Older adults (≥65 years) were recruited, and cognitive ability was measured using the Montreal Cognitive Assessment (MoCA). Participants performed repetitive elbow flexion under three conditions: 1) at maximum pace alone (Single-task); and 2) while counting backward by ones (Dual-task 1); and 3) threes (Dual-task 2). Similar single- and dual-task gait were performed at normal speed. Three-dimensional kinematics were measured for both motor functions using wearable sensors. Results: One-hundred older adults participated in this study. Based on MoCA score <20, 21 (21%) of the participants were considered cognitively impaired (mean age =86±10 and 85±5 for cognitively impaired and intact participants, respectively). Within ANOVA models adjusted with demographic information, UEF dual-task parameters, including speed and range-of-motion variability were significantly higher by 52% on average, among cognitively impaired participant (p<0.01). Logistic models with these UEF parameters plus age predicted cognitive status with sensitivity, specificity, and area under curve (AUC) of 71%, 81% and 0.77 for Dual-task 1. The corresponding values for UEF Dual-task 2 were 91%, 73% and 0.81, respectively. ANOVA results were non-significant for gait parameters within both dual-task conditions (p>0.26). Conclusion: This study demonstrated that counting backward by threes within a UEF dual-task experiment was a pertinent and challenging enough task to detect cognitive impairment in older adults. Additionally, UEF was superior to gait as the motor task component of the dual-task. The UEF dual-task could be applied as a quick memory screen in a clinical setting. Keywords: wearable motion sensor, gait, upper-extremity function, biomechanics, MCI, Alzheimer’s disease
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".