Development and Validation of Machine Learning Models for Predicting Early Cognitive Decline Using Home Sensor-Derived Behavioral Data: Sensors in-Home for Elder Wellbeing (SINEW) Cohort Study

Clinical Snapshot

Key Findings

• P Value: Not applicable - study ongoing

• Effect Size: Not applicable - study ongoing

• Primary Outcome: No results available - protocol paper only

• Nnt Or Sensitivity: Target metrics include AUC, sensitivity, specificity - results pending

• Confidence Interval: Not applicable - study ongoing

Clinical Application

Requires significant technological infrastructure and ongoing technical support; may be challenging in routine clinical practice Relevant to Australian aged care settings; could complement existing cognitive screening programs but would require integration with current healthcare pathways and consideration of technology literacy among older Australians Community-dwelling older adults aged 65+ with normal cognition or mild cognitive impairment

Abstract

BACKGROUND: As the global population continues to age, the prevalence of geriatric conditions, including dementia and frailty, is also increasing. Early identification of individuals at an elevated risk of these conditions, such as those presenting with mild cognitive impairment (MCI) or prefrailty, can provide a critical window for prompt intervention aimed at preventing or reversing disease progression. To promote such early identification, there is a burgeoning interest in the use of digital sensor technology and predictive modeling. OBJECTIVE: This study aimed to use a continuous, home-based monitoring sensor system for older adults to distinguish those exhibiting normal aging from those with MCI, early dementia, prefrailty, or frailty, and to predict their transition from normal aging to one of these conditions. METHODS: This longitudinal cohort study will recruit 200 community-dwelling adults aged ≥65 years with normal cognition or MCI at baseline. A multi-sensor system will be installed in participants' homes, including passive infrared motion sensors, door contact sensors, bed sensors, medication box sensors, wearable activity bands, and Bluetooth proximity beacons. These devices will continuously capture spatiotemporal activity patterns, mobility indicators, sleep behaviors, and medication-taking routines. Annual assessments will include standardized cognitive tests (eg, Montreal Cognitive Assessment, Mini-Mental State Examination, Rey Auditory-Verbal Learning Test, digit span, Color Trails Test, semantic fluency, Stroop), frailty measures (modified Fried phenotype, gait speed, grip strength), mental health scales, sleep quality, and psychosocial indicators. Sensor-derived features-such as gait variability, activity regularity, sleep fragmentation, and medication adherence patterns-will be integrated with clinical data to develop supervised machine learning models. Planned approaches include logistic regression, random forests, gradient boosting, and deep learning. Model performance will be evaluated using cross-validation and independent test sets. Primary metrics will include area under the receiver operating characteristic curve, sensitivity, specificity, precision, recall, and F1-score. Models will be benchmarked against gold-standard clinical diagnoses and validated using temporal subsets of the dataset. RESULTS: Enrollment for this study started in November 2019 and will continue until March 2030. As of June 2025, we have enrolled 138 participants. Full data analysis has yet to begin. CONCLUSIONS: We aim to develop a reliable and effective sensor system for in-home use that will facilitate the early detection of cognitive and physical decline. In so doing, it will add to our current understanding of digital biomarkers. It is common for older adults to seek clinical intervention only when their cognitive impairment has already reached an advanced stage. The implementation of readily deployable sensor systems within community settings presents us with opportunities for prompt intervention, which holds the potential for delaying or reversing disease progression and allowing for a greater number of functional and meaningful years.

References

1. 1.Moon, J. P., Abdul Jabbar, K., Tay, T. C. I., Tay, L., Mahendran, R., Ng, T. P., Tan, S. M., Koh, W. Z. H., Ying, W. Y., Tan, A. H., Lee, T. S., & Rawtaer, I. (2026). Development and validation of machine learning models for predicting early cognitive decline using home sensor-derived behavioral data: Sensors in-Home for Elder Wellbeing (SINEW) cohort study. JMIR Research Protocols. https://doi.org/10.1162/neco.1997.9.8.1735