Abstract Details

Title Clinical Decision Support Tool Integrating Dual-Task Gait Biomarkers Predicts Cognitive Decline Progression in Alzheimer's Disease and Mild Cognitive Impairment: A 6-Month Pilot Study.

Topic Aging, Dementia, and Behavioral Neurology

Presentation(s) P4 - Poster Session 4 (8:00 AM-9:00 AM)

Poster/Presentation
Number 12-014

Objective

To validate a clinical decision support tool integrating dual-task gait biomarkers with cognitive assessment and evaluate predictive validity for 6-month cognitive decline progression.

Background

Dual-task gait reveals early executive dysfunction in prodromal AD/MCI but remains underutilized due to a lack of standardized implementation tools. No prior studies quantified the clinical value of dual-task gait in automated decision support systems.

Design/Methods

Prospective pilot study of 25 participants (8 AD, 9 MCI, 8 controls; age 71.8±7.2 years; 52% female) with 6-month follow-up. Portable inertial sensors measured single-task and dual-task gait: stride velocity, stride time variability (CV%), and dual-task cost. Tool-generated risk stratification using validated thresholds: dual-task velocity <85 cm/s, CV >4.5%, dual-task cost >25%. A neurologist evaluated participants with/without tool assistance. Outcomes: diagnostic accuracy, clinician confidence, time-to-diagnosis, and 6-month progression prediction (≥3-point MoCA decline or MCI→AD conversion).

Results

The Tool improved diagnostic accuracy from 68.0% to 88.0% (p=0.019; 20.0% improvement). MCI detection: 55.6% to 88.9% (p=0.003; OR=6.40, 95% CI: 1.82-22.5). Dual-task metrics added 4.0% accuracy beyond single-task (p=0.041). Clinician confidence increased from 3.2±0.8 to 4.6±0.5 (p<0.001, Cohen's d=2.12). At 6-months, high-risk participants showed 85.7% progression versus 20.0% in low-risk (p=0.006; RR=4.29). Dual-task predicted progression with sensitivity 87.5%, specificity 88.9%, and AUC 0.92. Positive predictive value: 85.7%; negative predictive value: 80.0%. Inter-rater reliability: κ=0.87.

Conclusions

Decision support tool integrating dual-task gait improved diagnostic accuracy (OR=6.40 for MCI) and predicted 6-month progression with 85.7% PPV (HR=6.83). Findings support a clinical validation for standard-of-care implementation and the use of Software as a Medical Device.

Authors/Disclosures
Rhea Doshi, Student PRESENTER	Miss Doshi has nothing to disclose.
Ashkan Novin, PhD	Dr. Novin has received personal compensation for serving as an employee of RNP. Dr. Novin has received intellectual property interests from a discovery or technology relating to health care. Dr. Novin has received intellectual property interests from a discovery or technology relating to health care. Dr. Novin has received intellectual property interests from a discovery or technology relating to health care.
Roshni N. Patel, MD (CENTEROF EXCELLENCE IN PAIN AND REGENERATIVE MEDICINE)	Dr. Patel has received personal compensation in the range of $10,000-$49,999 for serving as an Expert Witness for RNP MEDICAL .

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