A study led by the Johns Hopkins Bloomberg School of Public Health has introduced a novel approach to potentially detecting early signs of Alzheimer’s disease through daily activity monitoring.
By analyzing the movement data from devices similar to wristwatches, known as actigraphs, worn by 82 older adults, researchers have uncovered patterns that may serve as early indicators of Alzheimer’s, specifically among individuals showing signs of amyloid buildup in the brain, a hallmark of the disease.
Participants in this research, all of whom were considered cognitively healthy, were part of a long-term aging study. They were divided based on the presence or absence of brain amyloid, detected through PET scans.
The analysis revealed notable differences in activity levels and consistency between those with amyloid deposits and those without, especially during certain afternoon hours.
These findings, published in the journal SLEEP, build on previous research that also identified unusual activity patterns in individuals with amyloid buildup.
The use of actigraphs and a sophisticated statistical method called function-on-scalar regression (FOSR) allowed the researchers to pinpoint these differences, suggesting that such devices could eventually become tools for early Alzheimer’s detection before significant cognitive decline occurs.
Alzheimer’s disease affects millions and is characterized by amyloid plaques and tau tangles in the brain. The disease process starts long before symptoms become apparent, highlighting the importance of early detection methods.
Current treatments targeting amyloid beta have the potential to slow disease progression if applied early in the disease course. This underscores the potential value of identifying early biomarkers, such as altered daily activity patterns, that could signal the onset of Alzheimer’s.
The study also touches on the relationship between sleep-wake cycles and Alzheimer’s, with abnormal patterns previously linked to the disease.
Given that amyloid accumulation may disrupt these rhythms early on, and that disrupted sleep can further accelerate amyloid deposition, tracking activity could offer a non-invasive way to spot early disease signs.
The participants were part of the Baltimore Longitudinal Study of Aging, highlighting the study’s robust foundation.
Despite these promising results, the researchers caution against overinterpreting the findings for individual diagnosis based on activity patterns alone, emphasizing the need for further research to confirm these associations and explore their implications for early Alzheimer’s screening and intervention.
This study not only contributes to our understanding of Alzheimer’s disease and its early indicators but also opens up discussions about the role of technology and data analysis in future healthcare, particularly for conditions like Alzheimer’s where early detection can significantly influence treatment outcomes.
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The research findings can be found in SLEEP.
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