How Sleep Conditions Predict the Likelihood and Severity of Falls in Older Adults

CONFERENCE REPORTER

Jesse Cook, PhD, a clinical psychologist and sleep researcher at the University of Wisconsin-Madison, focused on the relationship between sleep and healthy aging, particularly the risk of falls in older adults for his presentation at the SLEEP 2026 conference in Baltimore, Maryland.¹

Cook highlighted that 1 in 4 older adults experience a fall annually, with 1 in 10 resulting in injury-related deaths,² costing $50 billion in 2020. Sleep health is recognized as a modifiable risk factor for various health outcomes, including falls. Cook's research examines specific modifiable sleep health factors like excessive daytime sleepiness and sleep inertia, which affect 20% and 10% of older adults, respectively. He aims to understand how these sleep conditions predict the likelihood and severity of falls, aiming to identify preventive measures. Factors such as short and long sleep duration, sleep apnea, insomnia, and sleep medication use have been linked to fall risk.

Sleep inertia, Cook explained, is a construct that describes the cognitive, physical, and psychological impairment experienced postawakening. The Centers for Disease Control and Prevention and National Institute for Occupational Safety and Health describe sleep inertia as, “temporary disorientation and decline in performance and/or mood after awakening from sleep,” and notes that, “People can show slower reaction time, poorer short-term memory, and slower speed of thinking, reasoning, remembering, and learning.”³ Most people experience sleep inertia for about 30 minutes, but some may experience debilitating symptoms for hours. Pathologic sleep inertia, characterized by prolonged symptoms, is a known safety hazard. Cook's research aims to better understand the relationship between sleep inertia and falls in older adults.

Dr Cook is a postdoctoral fellow with a doctoral degree in clinical psychology from the University of Wisconsin-Madison. His research focuses on the intersection of sleep and mental health, with his primary program of research purposed to advance the classification, assessment, and treatment of unexplained hypersomnolence. He has additional research interests related to the strengths, limitations, and overall utility of wearable sleep tracking technology for research and clinical purposes, as well as the roles of sleep and circadian health in the performance and well-being of athletes.

References

1. Cook J, Barnet J, Love J, et al. 0806 Sleep inertia, daytime sleepiness, and falls in older adults of the Wisconsin sleep cohort. Sleep. 2026;49(Suppl 1):A361.

2. Older adult falls data. CDC. February 26, 2026. Accessed June 12, 2026. https://www.cdc.gov/falls/data-research/index.html

3. NIOSH training for nurses on shift work and long work hours. National Institute for Occupational Safety and Health. March 31, 2020. Accessed June 12, 2026. https://www.cdc.gov/niosh/work-hour-training-for-nurses/longhours/mod7/03.html