The Effects of Exercise on Sleep and Brain Health
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Aging
- Sponsor
- Massachusetts General Hospital
- Enrollment
- 26
- Locations
- 1
- Primary Endpoint
- Change from baseline in cognitive performance (as measured via the National Institutes of Health (NIH) Toolbox Cognition battery) after a 12-week aerobic exercise program.
- Status
- Completed
- Last Updated
- 4 years ago
Overview
Brief Summary
Brain health and cognitive functioning can be affected by aging. Exercise is a potentially effective method for promoting "successful brain aging" by improving cardiovascular fitness, brain function and possibly sleep quality. This project will measure the effects of exercise on brain health and attempt to develop a better way to track brain health, by measuring brain activity during sleep.
Detailed Description
There exists a critical need to develop biomarkers of brain age and for scientifically proven interventions to improve brain health. Previously, a machine learning algorithm, the Brain Age Index (BAI), was developed to predict brain age (BA) based on 510 features derived from an overnight sleep EEG. The algorithm reports how old an individual's sleeping brain activity "looks", called the "brain age" (BA), and compares this with the chronological age (CA). The difference is the BAI: BAI = BA-CA. Prior work suggests that patients with significant neurological or psychiatric disease or hypertension and diabetes exhibit a mean excess brain age, or "brain age index" (BAI), of 4 and 3.5 years relative to healthy controls. Moreover, it has been shown that high BAI is an independent predictor of mortality. Each extra year of BAI yields a 3.3% relative increase in the risk of death. Work from other groups suggest that exercise is potentially effective for promoting "successful brain aging". Studies of exercise effects on cognition include a metanalysis of 18 prior studies that analyzed the results of exercise on cognitive function in older adults. It was found that aerobic fitness training improved performance across several cognitive domains, including executive function, cognitive control, spatial processing, and processing speed, with an average improvement across studies and across all domains of 0.5 standard deviations relative to controls. Improvement was greatest for executive and control processes. The degree of improvement was also related to the length of the fitness-training intervention, duration of training sessions, and gender (females appeared to benefit more). Studies of exercise effects on brain structure include a prior study that enrolled 35 older adults (14 with Mild Cognitive Impairment, 16 healthy controls) to participate in a 12-week moderate-intensity walking program. Subjects' VO2max increased by an average of 8.49%. The degree to which cardiorespiratory fitness (V̇O2peak) improved due to the intervention was strongly positively correlated with widespread changes in cortical thickness. Taken together, these and other studies suggest that aerobic exercise may be an effective intervention to counteract cortical atrophy due to aging and disease and might provide protection against future cognitive decline in at-risk older adults. This study hypothesizes that cognitive performance will increase after 12 weeks of regular exercise (1a), EEG-based BAI will be lower after 12 weeks of regular exercise (1b), and improvements of cognitive measures are predictable from changes in BAI (1c). Additionally, it is hypothesized that an excess BAI will correlate with poor sleep quality, higher pre-existing comorbidities, poor diet, and small social network (2). Sedentary subjects who undergo the 12-week exercise training program are anticipated to show measurable improvements in EEG-based brain age and cognitive function, and that the degree of improvement will be related to the degree of improvement in aerobic fitness. This study will provide preliminary data to support a larger and longer longitudinal study designed to 1) Clinically validate novel, low-cost, and patient-friendly EEG-based biomarkers of brain health; and 2) Assess the effectiveness of interventions aimed at preserving and improving brain health and ultimately extending healthspan.
Investigators
Michael Brandon Westover
Associate Professor of Neurology, Harvard Medical School & Principal Investigator
Massachusetts General Hospital
Eligibility Criteria
Inclusion Criteria
- •Sedentary (≤ two exercise sessions per week for the past 6 months)
- •Aged 50 to 75 years old
- •Cleared by primary care physician or other personal physician to participate in a 12-week moderate-intensity walking exercise program. Clearance can be provided to one of the study investigators either verbally or in writing.
Exclusion Criteria
- •History of neurological illness (e.g. poorly controlled epilepsy with \>1 seizure per month in the last 6mo, stroke with residual motor language deficits, Multiple sclerosis, Parkinson's disease, clinically diagnosed dementia \[defined as score \<26 on the Mini-Mental State Examination\], head trauma in the preceding 6-months with continued cognitive symptoms, cerebral palsy, brain tumor, normal-pressure hydrocephalus, HIV infection, or Huntington's disease)
- •Untreated Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) Axis I disorders (i.e., severe depressive symptoms, substance abuse or dependence)
- •Impaired activities of daily living (ADLs) measured by the Lawton and Brody Self-Maintaining and Instrumental Activities of Daily Living Scale.
- •Inability to safely exercise or perform any of the tests
- •Inability to perform the cognitive tests due to lack of English proficiency
- •Known diagnosis of severe sleep apnea (apnea-hypopnea index ≥ 15/hour of sleep)
- •Subject fails Cardiopulmonary Exercise Testing (CPET), i.e. develops symptoms such as shortness of breath, chest pain, palpitations, lightheadedness, or syncope during CPET testing
- •Patients with a pacemaker or an automatic implantable cardioverter-defibrillator.
Outcomes
Primary Outcomes
Change from baseline in cognitive performance (as measured via the National Institutes of Health (NIH) Toolbox Cognition battery) after a 12-week aerobic exercise program.
Time Frame: Baseline, 12 weeks
The NIH Toolbox Cognition battery is a composite of 7 tests \[Picture Vocabulary (PV), Reading Test (RT), Flanker, Dimensional Change Card Sort (DCCS), Picture Sequence Memory (PSM), List Sorting (LS), Pattern Comparison (PC)\] assessing language, receptive vocabulary, executive function, attention, working/short-term/episodic memory, cognitive flexibility, processing speed, prior education, verbal intelligence. Uncorrected, Age-corrected, and Fully-Corrected scores (mean =100,100,50 and StdDev=15,15,10 respectively) are calculated for each test. Composite scores are given for Fluid, Crystallized, and overall Cognitive Function. The Fluid Composite Score is derived by averaging the std. scores of the Flanker, DCCS, PSM, LS and PC tests. The Crystallized Composite Score is derived by averaging the std. scores of the PV \& RT. The Cognitive Function Composite Score is derived by averaging the Fluid \& Crystallized std. scores. Higher scores indicate higher levels of cognitive functioning.
Change from baseline in EEG-based Brain Age (as measured via the Brain Age Index algorithm) after a 12-week aerobic exercise program.
Time Frame: Baseline, 12 weeks
The Brain Age Index (BAI) algorithm reports how old an individual's sleeping brain activity "looks", called the "brain age" (BA), and compares this with the chronological age (CA). The difference is the Brain Age Index (BAI), which is calculated by subtracting the Chronological Age (CA) from the calculated Brain Age (BA): BAI = BA-CA. A higher BAI can reflect worse clinical outcomes (e.g. increase mortality risk) while a lower BAI can reflect better clinical outcomes.
Secondary Outcomes
- Association between brain age index (BAI) and socioeconomic status (as measured via The Hollingshead Four Factor Index of Social Status).(Baseline)
- Association between brain age index (BAI) and sleep quality (as measured via a home sleep EEG monitoring device).(Weeks 1-12)
- Association between brain age index (BAI) and depression (as measured via the Patient Health Questionnaire-2).(Baseline)
- Association between brain age index (BAI) and anxiety (as measured via the Generalized Anxiety Disorder Questionnaire-2).(Baseline)
- Association between brain age index (BAI) and diet (as measured via the 14-Q Mediterranean Diet Questionnaire).(Baseline)
- Association between brain age index (BAI) and sleep quality (as measured via respiration).(Weeks 1-12)
- Association between brain age index (BAI) and pre-existing co-morbidity (as measured via the Charlson Co-morbidity Index).(Baseline)
- Association between brain age index (BAI) and social network (as measured via the Social Network Index).(Baseline)
- Association between brain age index (BAI) and social network (as measured via the University of California, Los Angeles Loneliness Scale).(Baseline)