Exploring Digital Biomarkers for Dementia Care #Sciencefather #Researcherawards



Introduction

Inactivity and the rise of psychological and behavioral symptoms among individuals with dementia pose major challenges in clinical care. Traditional assessment methods depend heavily on proxy-rated tools, which may not fully capture the real-time physiological and behavioral fluctuations of patients. To overcome this limitation, the integration of sensor-based digital biomarkers offers a promising avenue for continuous, objective, and non-invasive monitoring. This study focuses on evaluating the feasibility and adherence of wearable and environmental sensing technologies—specifically the Garmin Vivoactive5 and Somnofy systems—to explore the relationship between daytime activity levels and sleep quality in nursing home residents with dementia.

Research Objective and Rationale

The primary goal of this research is to investigate the interconnection between daytime activity and sleep parameters using sensor technologies. Understanding this relationship is crucial, as poor sleep patterns and inactivity contribute to worsening behavioral symptoms and cognitive decline. By utilizing digital biomarkers such as sleep efficiency (SE), sleep score, sleep regularity index (SRI), and wake after sleep onset (WASO), this study aims to provide an objective framework for identifying behavioral changes, offering a potential replacement or supplement to subjective, proxy-based evaluations like the PSMS and NPI-NH scales.

Methodological Framework

A cohort of 11 nursing home residents diagnosed with moderate to severe dementia participated in this study. Over 42 day–night data pairs were analyzed using the Garmin Vivoactive5 and Somnofy devices. Physical activity and sleep data were continuously recorded and cross-correlated with standard proxy-rated tools. Spearman’s correlation coefficients were applied to assess the relationships between digital biomarkers and traditional clinical measures, ensuring statistical validity and reliability in understanding behavioral dynamics.

Key Findings and Correlations

Results revealed significant associations between daytime activity and several sleep parameters. Specifically, higher daytime activity levels were inversely correlated with WASO (−0.34, p = 0.03) and positively correlated with SRI (0.43, p = 0.01). Additionally, traditional sleep measures such as WASO were found to be associated with the NPI-NH-K scores (p = 0.03). These findings suggest that increased daytime physical activity may enhance sleep regularity and reduce nocturnal restlessness among individuals with dementia.

Implications for Dementia Care and Clinical Practice

The study underscores the feasibility of using sensing technologies in long-term care settings for dementia patients. These tools can identify subtle variations in sleep and activity patterns that are often missed by proxy-rated assessments. Implementing sensor-based monitoring can lead to earlier detection of behavioral changes, enabling caregivers and clinicians to tailor interventions more effectively. Such integration represents a step toward precision care in dementia management, enhancing both the quality of life for residents and the decision-making process for healthcare professionals.

Future Research Directions

While the current findings provide valuable insights, the bidirectional relationship between daytime activity and sleep quality remains ambiguous. Future research should involve larger sample sizes, longer observation periods, and the inclusion of cognitive and emotional state indicators. Investigating causality and integrating multi-sensor data could refine predictive models for dementia progression. Moreover, enhancing device usability and addressing ethical concerns surrounding data privacy will be essential for widespread clinical adoption.

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#Sciencefather, #Reseacherawards, #DigitalBiomarkers, #DementiaResearch, #SleepQuality, #DaytimeActivity, #WearableTechnology, #SensorTechnology, #NeurodegenerativeDisorders, #Gerontology, #BehavioralScience, #CognitiveHealth, #NursingHomeCare, #SmartHealthcare, #SleepScience, #GeriatricMedicine, #HealthMonitoring, #BiomedicalEngineering, #AIinHealthcare, #ClinicalAssessment, #ElderlyCare, #ScienceFather,

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