Rest-activity Rhythms and Cognitive Impairment and Dementia in Older Women

Results From the Women's Health Initiative

Qian Xiao PhD; Aladdin H. Shadyab PhD; Stephen R. Rapp PhD; Katie L. Stone PhD; Kristin Yaffe MD; Joshua N. Sampson PhD; Jiu-Chiuan Chen MD, ScD; Kathleen M. Hayden PhD; Victor W. Henderson MD; Andrea Z. LaCroix PhD


J Am Geriatr Soc. 2022;70(10):2925-2937. 

In This Article

Abstract and Introduction


Introduction: Growing evidence suggests that impairment in rest-activity rhythms may be a risk factor for cognitive decline and impairment in the aging population. However, previous studies included only a limited set of rest-activity metrics and produced mixed findings. We studied a comprehensive set of parametric and nonparametric characteristics of rest-activity rhythms in relation to mild cognitive impairment (MCI) and probable dementia in a cohort of older women.

Methods: The prospective analysis included 763 women enrolled in two ancillary studies of the Women's Health Initiative (WHI): the WHI Memory Study-Epidemiology of Cognitive Health Outcomes and Objective Physical Activity and Cardiovascular Health studies. The association between accelerometry-based rest-activity parameters and centrally adjudicated MCI and probable dementia were determined using Cox regression models adjusted for sociodemographic characteristics, lifestyle factors, and comorbidities.

Results: Overall, the results support a prospective association between weakened rest-activity rhythms (e.g., reduced amplitude and overall rhythmicity) and adverse cognitive outcomes. Specifically, reduced overall rhythmicity (pseudo F statistic), lower amplitude and activity level (amplitude/relative amplitude, mesor, and activity level during active periods of the day [M10]), and later activity timing (acrophase and midpoint of M10) were associated with a higher risk for MCI and probable dementia. Women with lower amplitude and mesor also exhibited faster cognitive decline over follow-up.

Conclusion: Weakened rest-activity rhythms may be predictive markers for cognitive decline, MCI, and dementia among older women.


Many human behaviors exhibit diurnal fluctuations that are orchestrated by the internal circadian timing system and influenced by external stimuli such as light, work schedules, and social patterns.[1] A prominent example of human diurnal behavior is the rest-activity rhythm, and alterations in rest-activity rhythms have been linked to many chronic diseases.[2,3] The process of aging is associated with a decline in circadian output and shift in circadian phase,[4,5] leading to impairment in rest-activity rhythms in older adults. Some evidence suggests that older women may be disproportionally affected by circadian dysfunction and related diseases.[4,6]

A limited but growing number of epidemiological studies have reported that weakened rest-activity rhythms, typically manifested as lower amplitude, reduced overall rhythmicity, increased variability but decreased stability, and altered timing, were associated with a higher risk of dementia and/or mild cognitive impairment (MCI).[7–11] The two main methods for characterizing rest-activity rhythms are cosine-based parametric models[12] and nonparametric algorithms.[13] The former assumes a cosine or cosine-like shape of daily activity patterns and produces rhythmic measures such as amplitude (0.5*difference between the peak and nadir of daily activity levels), mean estimating statistic of rhythm (mesor), acrophase (timing of activity peak) and overall rhythmicity. In contrast, the nonparametric methods have no underlying assumption about activity patterns and derive metrics that measure specific aspects of the rest-activity cycles, such as stability, variability/fragmentation, and activity levels and timing during the most and least active periods of the rest-activity cycle. The two methods are complementary to each other and together offer a more comprehensive assessment of the rest-activity rhythm. However, most of the previous studies only used either the parametric or nonparametric method, and even when both methods were applied,[9] the list of metrics examined tended to be limited. Therefore, there is a need for additional investigation combining both approaches and therefore including a more complete set of rest-activity characteristics to study their relationships with the risk of dementia and cognitive impairment in older populations.

We examined associations of rest-activity characteristics with incident probable dementia and MCI in the Women's Health Initiative (WHI) study. Our study included both parametric and nonparametric methods and thus employed an agnostic approach to examine multiple aspects of the rest-activity rhythm. We hypothesized that patterns of weakened rest-activity rhythms would be associated with a higher risk of MCI and probable dementia in older women.