Study Objectives To compare sleep and circadian rest/activity rhythms (RARs), quantified by standard and novel actigraphic metrics, between controls and participants with mild cognitive impairment (MCI), and to examine the cross-sectional relationships between these measures and cognition. Methods Actigraphy data were collected in 179 older individuals (mean age = 72.6 years) with normal cognition (n = 153) and MCI (n = 26). Sleep parameters (e.g. sleep efficiency), and standard nonparametric RARs (e.g. interdaily stability) were generated. Functional principal component analysis (fPCA) was used to generate three novel RAR metrics (fPC1, fPC2, and fPC3). Cognitive composite scores reflecting episodic memory and executive function were derived using factor analysis. Regression models compared sleep and RAR parameters between diagnostic groups and their association with cognitive performance. Results Compared to controls, the MCI group exhibited lower levels of the standard RAR parameter: relative amplitude and fPC3—a novel RAR whereby lower scores reflected a lower rhythm peak, as well as greater nighttime activity and less activity in the morning. Across groups, several standard RAR parameters (e.g. interdaily stability) and fPC3 were associated with better episodic memory and executive function performance. Additionally, several standard RAR measures (e.g. relative amplitude) and the novel RAR measure fPC1 (reflecting the total volume of activity and rhythm strength) were associated with better executive function performance. Conclusions Individuals with MCI have altered circadian RARs compared to controls, including the novel RAR metric fPC3, reflecting greater nighttime activity and less activity in the morning compared to mean values. Additionally, these measures are significantly associated with cognitive performance.

Study Objectives To examine in a subsample at the screening phase of a clinical trial of an β-amyloid (Aβ) antibody whether disturbed sleep and altered 24-hour rest/activity rhythms (RARs) may serve as markers of preclinical Alzheimer’s disease (AD). Methods Overall, 26 Aβ-positive (Aβ+) and 33 Aβ-negative (Aβ-) cognitively unimpaired participants (mean age = 71.3±4.6 years, 59% women) from the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s (A4) and the Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) studies, respectively, wore actigraphs for 5.66±0.88 24-hour periods. We computed standard sleep parameters, standard RAR metrics (mesor, amplitude, acrophase, interdaily stability, intradaily variability, relative amplitude), and performed a novel RAR analysis (function-on-scalar regression (FOSR)). Results We were unable to detect any differences between Aβ+ or Aβ- participants in standard sleep parameters or RAR metrics with our sample size. When we used novel FOSR methods, however, Aβ+ participants had lower activity levels than Aβ- participants in the late night through early morning (11:30 PM to 3:00 AM), and higher levels in the early morning (4:30 AM to 8:30 AM) and from midday through late afternoon (12:30 PM to 5:30 PM; all p <0.05). Aβ+ participants also had higher variability in activity across days from 9:30 PM to 1:00 AM and 4:30 AM to 8:30 AM, and lower variability from 2:30 AM to 3:30 AM (all p <0.05). Conclusions Although we found no association of preclinical AD with standard actigraphic sleep or RAR metrics, a novel data-driven analytic method identified temporally “local” RAR alterations in preclinical AD.

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Background Poor sleep may increase the likelihood of fatigue, and both are common in later life. However, prior studies of the sleep–fatigue relationship used subjective measures or were conducted in clinical populations; thus, the nature of this association in healthier community-dwelling older adults remains unclear. We studied the association of actigraphic sleep parameters with perceived fatigability—fatigue in response to a standardized task—and with conventional fatigue symptoms of low energy or tiredness. Methods We studied 382 cognitively normal participants in the Baltimore Longitudinal Study of Aging (aged 73.1 ± 10.3 years, 53.1% women) who completed 6.7 ± 0.9 days of wrist actigraphy and a perceived fatigability assessment, including rating of perceived exertion (RPE) after a 5-minute treadmill walk or the Pittsburgh Fatigability Scale (PFS). Participants also reported non-standardized symptoms of fatigue. Results After adjustment for age, sex, race, height, weight, comorbidity index, and depressive symptoms, shorter total sleep time (TST; <6.3 hours vs intermediate TST ≥6.3 to 7.2 hours) was associated with high RPE fatigability (odds ratio [OR] = 2.56, 95% confidence interval [CI] = 1.29, 5.06, p = .007), high PFS physical (OR = 1.88, 95% CI = 1.04, 3.38, p = .035), and high mental fatigability (OR = 2.15, 95% CI = 1.02, 4.50, p = .044), whereas longer TST was also associated with high mental fatigability (OR = 2.19, 95% CI = 1.02, 4.71, p = .043). Additionally, longer wake bout length was associated with high RPE fatigability (OR = 1.53, 95% CI = 1.14, 2.07, p = .005), and greater wake after sleep onset was associated with high mental fatigability (OR = 1.14, 95% CI = 1.01, 1.28, p = .036). Conclusion Among well-functioning older adults, abnormal sleep duration and sleep fragmentation are associated with greater perceived fatigability.

Sleep and physical activity, two important health behaviors, are often studied independently using different accelerometer types and body locations. Understanding whether accelerometers designed for monitoring each behavior can provide similar sleep parameter estimates may help determine whether one device can be used to measure both behaviors. 331 adults (70.7±13.7 years) from the Baltimore Longitudinal Study of Aging (BLSA) wore the ActiGraph GT9X Link and the Actiwatch 2 simultaneously on the non-dominant wrist for 7.0±1.6 nights. Total sleep time (TST), wake after sleep onset (WASO), sleep efficiency, number of wake bouts, mean wake bout length, and sleep fragmentation index (SFI) were extracted from ActiGraph using the Cole-Kripke algorithm and from Actiwatch using the software default algorithm. These parameters were compared using paired t-tests, Bland-Altman plots, and Deming regression models. Stratified analyses were performed by age, sex, and BMI. Compared to the Actiwatch, the ActiGraph estimated comparable TST and sleep efficiency, but fewer wake bouts, longer WASO, longer wake bout length, and higher SFI (all p<0.001). Both devices estimated similar 1-minute and 1% differences between participants for TST and SFI (β=0.99, 95% CI: 0.95, 1.03, and 0.91, 1.13, respectively), but not for other parameters. These differences varied by age, sex, and/or BMI. The ActiGraph and the Actiwatch provide comparable absolute and relative estimates of TST, but not other parameters. The discrepancies could result from device differences in movement collection and/or sleep scoring algorithms. Further comparison and calibration is required before these devices can be used interchangeably.