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Sleep and physical activity levels are both associated with cognitive performance among older adults; however, the brain mechanisms underlying these beneficial relationships remain poorly understood. This study investigated cross-sectional associations of actigraphic estimates of physical activity and sleep with cognition and diffusion imaging-based measures of medial temporal lobe (MTL) gray matter microstructural integrity in adults free of dementia. Participants were 132 older adults from the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) cohort study (119 cognitively unimpaired and 13 with mild cognitive impairment; mean age=70.8 years). Multiple linear regression analyses assessed the relationships between total volume of physical activity (TVPA), total sleep time (TST), and sleep efficiency (SE) with cognitive performance and MTL microstructural integrity. Results indicated that greater TVPA and SE were both independently associated with higher hippocampal and parahippocampal microstructure integrity (indicated by lower mean diffusivity) and better visuospatial processing abilities, independent of the volume of these structures and of amyloid burden, measured by positron emission tomography. Additionally, higher hippocampal microstructure statistically mediated the independent associations of physical activity and sleep with visuospatial abilities, independent of MTL volume and Aβ load. These findings suggest that physical activity and sleep are independently associated with cognitive performance, and that hippocampal microstructural integrity may be an underlying mechanism supporting these associations., Competing Interests: Conflict of Interest Vadim Zipunnikov consults with Takeda Pharmaceuticals. The details of the contracts are disclosed through the Johns Hopkins University eDisclose system and have no direct or apparent relationship with the current paper. Adam Spira received payment for serving as a consultant for Merck, received honoraria from Springer Nature Switzerland AG for guest editing special issues of Current Sleep Medicine Reports, and is a paid consultant to Sequoia Neurovitality, BellSant, Inc., and Amissa, Inc. These roles have no direct or apparent relationship with the current paper. Arnold Bakker is a paid consultant to AgeneBio, Inc. Arnold Bakker’s role in the current study was in compliance with the conflict of interest policies of the Johns Hopkins School of Medicine. His consulting role has no direct or apparent relationship with the current paper. The authors verify that each of the following statements is true pertaining to the submitted manuscript., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Although prior studies have examined associations of personality traits with sleep, most have investigated self-reported sleep, been cross-sectional, and focused on younger and middle-aged adults. We investigated associations of personality with actigraphic sleep parameters and changes in sleep in 398 cognitively normal adults aged 40-95 years (M ± SD = 70.1 ± 12.0) in the Baltimore Longitudinal Study of Aging. Participants completed the Revised NEO Personality Inventory (NEO-PI-R) and 6.61 days +/-1.01 nights of wrist actigraphy at the same study visit. Participants with wrist actigraphy at multiple study visits had actigraphy data at 3.11 ± 1.52 visits (follow-up = 2.35 ± 0.70 years). Adjusting for age, sex, race, education, depressive symptoms, comorbidities and interactions of these variables with time, greater extraversion was associated with higher sleep efficiency. After further adjustment for BMI, sleep medication use, and sleep apnea symptoms, greater extraversion was associated with shorter total sleep time, and greater openness was associated with shorter average wake bout length. We observed numerous interactions of personality with sex and age, with stronger personality-sleep associations generally present at younger ages (i.e., aged 50-60 vs. 70-80) and sex differences in associations. Middle-aged and older adults higher in extraversion and lower in openness may be more vulnerable to poor sleep and may benefit from screening for sleep disturbances., Competing Interests: Declarations. Competing interests: Adam Spira received payment for serving as a consultant for Merck, received honoraria from Springer Nature Switzerland AG for guest editing special issues of Current Sleep Medicine Reports, and is a paid consultant to Sequoia Neurovitality, BellSant, Inc. and Amissa, Inc. The other authors declare no competing interests., (© 2024. The Authors. Parts of this work were authored by US Federal Government authors and are not under copyright protection in the US; foreign copyright protection may apply. 2024.)

Importance: Accelerometry has been increasingly used as an objective index of sleep, physical activity, and circadian rhythms in people with mood disorders. However, most prior research has focused on sleep or physical activity alone without consideration of the strong within- and cross-domain intercorrelations; and few studies have distinguished between trait and state profiles of accelerometry domains in major depressive disorder (MDD)., Objectives: To identify joint and individual components of the domains derived from accelerometry, including sleep, physical activity, and circadian rhythmicity using the Joint and Individual Variation Explained method (JIVE), a novel multimodal integrative dimension-reduction technique; and to examine associations between joint and individual components with current and remitted MDD., Design, Setting, and Participants: This cross-sectional study examined data from the second wave of a population cohort study from Lausanne, Switzerland. Participants included 2317 adults (1164 without MDD, 185 with current MDD, and 968 with remitted MDD) with accelerometry for at least 7 days. Statistical analysis was conducted from January 2021 to June 2023., Main Outcomes and Measures: Features derived from accelerometry for 14 days; current and remitted MDD. Logistic regression adjusted for age, sex, body mass index, and anxiety and substance use disorders., Results: Among 2317 adults included in the study, 1261 (54.42%) were female, and mean (SD) age was 61.79 (9.97) years. JIVE reduced 28 accelerometry features to 3 joint and 6 individual components (1 sleep, 2 physical activity, 3 circadian rhythms). Joint components explained 58.5%, 79.5%, 54.5% of the total variation in sleep, physical activity, and circadian rhythm domains, respectively. Both current and remitted depression were associated with the first 2 joint components that were distinguished by the salience of high-intensity physical activity and amplitude of circadian rhythm and timing of both sleep and physical activity, respectively. MDD had significantly weaker circadian rhythmicity., Conclusions and Relevance: Application of a novel multimodal dimension-reduction technique demonstrates the importance of joint influences of physical activity, circadian rhythms, and timing of both sleep and physical activity with MDD; dampened circadian rhythmicity may constitute a trait marker for MDD. This work illustrates the value of accelerometry as a potential biomarker for subtypes of depression and highlights the importance of consideration of the full 24-hour sleep-wake cycle in future studies.

Background and Objectives: The aim of this study was to examine the diurnal links between average and changes in average levels of prospectively rated mood, sleep, energy, and stress as predictors of incident headache in a community-based sample., Methods: This observational study included structured clinical diagnostic assessment of both headache syndromes and mental disorders and electronic diaries that were administered 4 times per day for 2 weeks yielding a total of 4,974 assessments. The chief outcomes were incident morning (am) and later-day (pm) headaches. Generalized linear mixed-effects models were used to evaluate the average and lagged values of predictors including subjectively rated mood, anxiety, energy, stress, and sleep quality and objectively measured sleep duration and efficiency on incident am and pm headaches., Results: The sample included 477 participants (61% female), aged 7 through 84 years. After adjusting for demographic and clinical covariates and emotional states, incident am headache was associated with lower average (ß = -0.206*; confidence intervals: -0.397 to -0.017) and a decrease in average sleep quality on the prior day (ß = -0.172*; confidence interval: -0.305, -0.039). Average stress and changes in subjective energy levels on the prior day were associated with incident headaches but with different valence for am (decrease) (ß = -0.145* confidence interval: -0.286, -0.005) and pm (increase) (ß = 0.157*; confidence interval: 0.032, 0.281) headache. Mood and anxiety disorders were not significantly associated with incident headache after controlling for history of a diagnosis of migraine., Discussion: Both persistent and acute changes in arousal states manifest by subjective sleep quality and energy are salient precursors of incident headaches. Whereas poorer sleep quality and decreased energy on the prior day were associated with incident morning headache, an increase in energy and greater average stress were associated with headache onsets later in the day. Different patterns of predictors of morning and later-day incident headache highlight the role of circadian rhythms in the manifestations of headache. These findings may provide insight into the pathophysiologic processes underlying migraine and inform clinical intervention and prevention. Tracking these systems in real time with mobile technology provides a valuable ancillary tool to traditional clinical assessments.

Greater physical activity and better sleep are associated with reduced risk of cognitive decline and dementia among older adults, but little is known about their combined associations with measures of brain function and neuropathology. This study investigated potential independent and interactive cross-sectional relationships between actigraphy-estimated total volume of physical activity (TVPA) and sleep patterns [i.e., total sleep time (TST), sleep efficiency (SE)] with resting-state functional magnetic resonance imaging (rs-fMRI) measures of large scale network connectivity and positron emission tomography (PET) measures of amyloid-β. Participants were 135 non-demented older adults from the BIOCARD study (116 cognitively normal and 19 with mild cognitive impairment; mean age = 70.0 years). Using multiple linear regression analyses, we assessed the association between TVPA, TST, and SE with connectivity within the default-mode, salience, and fronto-parietal control networks, and with network modularity, a measure of network segregation. Higher TVPA and SE were independently associated with greater network modularity, although the positive relationship of SE with modularity was only present in amyloid-negative individuals. Additionally, higher TVPA was associated with greater connectivity within the default-mode network, while greater SE was related to greater connectivity within the salience network. In contrast, longer TST was associated with lower network modularity, particularly among amyloid-positive individuals, suggesting a relationship between longer sleep duration and greater network disorganization. Physical activity and sleep measures were not associated with amyloid positivity. These data suggest that greater physical activity levels and more efficient sleep may promote more segregated and potentially resilient functional networks and increase functional connectivity within specific large-scale networks and that the relationship between sleep and functional networks connectivity may depend on amyloid status., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: This study examined associations of actigraphy-estimated sleep parameters with concurrent and future cognitive performance in adults aged ≥ 50 years and explored interactions with race., Methods: Participants were 435 cognitively normal adults in the Baltimore Longitudinal Study of Aging who completed wrist actigraphy at baseline (mean = 6.6 nights) and underwent longitudinal testing of memory, attention, executive function, language, and visuospatial ability. On average, participants with follow-up data were followed for 3.1 years. Primary predictors were baseline mean total sleep time, sleep onset latency, sleep efficiency (SE), and wake after sleep onset (WASO). Fully adjusted linear mixed-effects models included demographics, baseline health-related characteristics, smoking status, sleep medication use, APOE e4 carrier status, and interactions of each covariate with time., Results: In adjusted models, higher SE (per 10%; B = 0.11, p = .012) and lower WASO (per 30 minutes; B = -0.12, p = .007) were associated with better memory cross-sectionally. In contrast, higher SE was associated with greater visuospatial ability decline longitudinally (B = -0.02, p = .004). Greater WASO was associated with poorer visuospatial ability cross-sectionally (B = -0.09, p = .019) but slower declines in visuospatial abilities longitudinally (B = 0.02, p = .002). Several sleep-cognition cross-sectional and longitudinal associations were stronger in, or limited to, Black participants (compared to White participants)., Conclusions: This study suggests cross-sectional sleep-cognition associations differ across distinct objective sleep parameters and cognitive domains. This study also provides preliminary evidence for racial differences across some sleep-cognition relationships. Unexpected directions of associations between baseline sleep and cognitive performance over time may be attributable to the significant proportion of participants without follow-up data and require further investigation., (© The Author(s) 2022. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Importance: Biologic systems involved in the regulation of motor activity are intricately linked with other homeostatic systems such as sleep, feeding behavior, energy, and mood. Mobile monitoring technology (eg, actigraphy and ecological momentary assessment devices) allows the assessment of these multiple systems in real time. However, most clinical studies of mental disorders that use mobile devices have not focused on the dynamic associations between these systems., Objectives: To examine the directional associations among motor activity, energy, mood, and sleep using mobile monitoring in a community-identified sample, and to evaluate whether these within-day associations differ between people with a history of bipolar or other mood disorders and controls without mood disorders., Design, Setting, and Participants: This study used a nested case-control design of 242 adults, a subsample of a community-based sample of adults. Probands were recruited by mail from the greater Washington, DC, metropolitan area from January 2005 to June 2013. Enrichment of the sample for mood disorders was provided by volunteers or referrals from the National Institutes of Health Clinical Center or by participants in the National Institute of Mental Health Mood and Anxiety Disorders Program. The inclusion criteria were the ability to speak English, availability to participate, and consent to contact at least 2 living first-degree relatives. Data analysis was performed from June 2013 through July 2018., Main Outcomes and Measures: Motor activity and sleep duration data were obtained from minute-to-minute activity counts from an actigraphy device worn on the nondominant wrist for 2 weeks. Mood and energy levels were assessed by subjective analogue ratings on the ecological momentary assessment (using a personal digital assistant) by participants 4 times per day for 2 weeks., Results: Of the total 242 participants, 92 (38.1%) were men and 150 (61.9%) were women, with a mean (SD) age of 48 (16.9) years. Among the participants, 54 (22.3%) had bipolar disorder (25 with bipolar I; 29 with bipolar II), 91 (37.6%) had major depressive disorder, and 97 (40.1%) were controls with no history of mood disorders. A unidirectional association was found between motor activity and subjective mood level (β = -0.018, P = .04). Bidirectional associations were observed between motor activity (β = 0.176; P = .03) and subjective energy level (β = 0.027; P = .03) as well as between motor activity (β = -0.027; P = .04) and sleep duration (β = -0.154; P = .04). Greater cross-domain reactivity was observed in bipolar disorder across all outcomes, including motor activity, sleep, mood, and energy., Conclusions and Relevance: These findings suggest that interventions focused on motor activity and energy may have greater efficacy than current approaches that target depressed mood; both active and passive tracking of multiple regulatory systems are important in designing therapeutic targets.

Background: We propose a method for estimating the timing of in-bed intervals using objective data in a large representative US sample, and quantify the association between these intervals and age, sex, and day of the week., Methods: The study included 11,951 participants 6 years and older from the National Health and Nutrition Examination Survey (NHANES) 2003-2006, who wore accelerometers to measure physical activity for seven consecutive days. Participants were instructed to remove the device just before the nighttime sleep period and put it back on immediately after. This nighttime period of non-wear was defined in this paper as the objective bedtime (OBT), an objectively estimated record of the in-bed interval. For each night of the week, we estimated two measures: the duration of the OBT (OBT-D) and, as a measure of the chronotype, the midpoint of the OBT (OBT-M). We estimated day-of-the-week-specific OBT-D and OBT-M using gender-specific population percentile curves. Differences in OBT-M (chronotype) and OBT-D (the amount of time spent in bed) by age and sex were estimated using regression models., Results: The estimates of OBT-M and their differences among age groups were consistent with the estimates of chronotype obtained via self-report in European populations. The average OBT-M varied significantly by age, while OBT-D was less variable with age. In the reference group (females, aged 17-22 years), the average OBT-M across 7 days was 4:19 AM (SD = 30 min) and the average OBT-D was 9 h 19 min (SD = 12 min). In the same age group the average OBT-D was 18 minutes shorter for males than for females, while the average OBT-M was not significantly different between males and females. The most pronounced differences were observed between OBT-M of weekday and weekend nights. In the reference group, compared to the average OBT-M of 3:50 am on Monday through Thursday nights, there was a 57-minute delay in OBT-M on Friday nights (entering the weekend), a 69-minute delay on Saturday nights (staying in the weekend), and a 23-minute delay on Sunday night (leaving the weekend). For both OBT-M and OBT-D, in most age groups and for most days of the week, there were no statistically significant differences between males and females, except for OBT-D on Wednesdays and Thursdays, with males having 31 (p-value < 0.05) and 45 (p-value < 0.05) minutes shorter OBT-D, respectively., Conclusions: The proposed measures, OBT-D and OBT-M, provide useful information of time in bed and chronotype in NHANES 2003-2006. They identify within-week patterns of bedtime and can be used to study associations between the bedtime and the large number of health outcomes collected in NHANES 2003-2006.