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INTRODUCTION Sleep and rest–activity rhythm alterations are common in neurodegenerative diseases. However, their characterization in patients with behavioral variant frontotemporal dementia (bvFTD) has proven elusive. We investigated rest–activity rhythm alterations, sleep disturbances, and their neural correlates in bvFTD. METHODS Twenty‐seven bvFTD patients and 25 healthy controls completed sleep questionnaires and underwent 7 days of actigraphy while concurrently maintaining a sleep diary. Cortical complexity and thickness were calculated from T1‐weighted magnetic resonance (MR) images. RESULTS Compared to controls, bvFTD patients showed longer time in bed (95% confidence interval [CI]: 79.31, 321.83) and total sleep time (95% CI: 24.38, 321.88), lower sleep efficiency (95% CI: −12.58, −95.54), and rest–activity rhythm alterations in the morning and early afternoon. Increased sleep duration was associated with reduced cortical thickness in frontal regions. DISCUSSION Patients with bvFTD showed longer sleep duration, lower sleep quality, and rest–activity rhythm alterations. Actigraphy could serve as a cost‐effective and accessible tool for ecologically monitoring changes in sleep duration in bvFTD patients. Highlights We assessed sleep and circadian rhythms in behavioral variant frontotemporal dementia (bvFTD) using actigraphy. Patients with bvFTD show increased sleep duration and reduced sleep quality. Patients with bvFTD show rest–activity alterations in the morning and early afternoon. Sleep duration is associated with reduced cortical thickness in frontal regions. These alterations may represent an early sign of neurodegeneration.

Objective Rest‐activity rhythms (RARs) are perturbed in many forms of neuropsychiatric illness. In this study, we applied wrist actigraphy to describe RAR perturbations in intellectually disabled adults with epilepsy (“E + ID”), using a cross‐sectional case–control design. We examined whether RAR phenotypes correlated with epilepsy severity, deficits in adaptive function, and/or comorbid psychopathology. Methods Caregivers of E + ID subjects provided informed consent during routine ambulatory clinic visits and were asked to complete standardized surveys of overall epilepsy severity (GASE, Global Assessment of Severity of Epilepsy), adaptive function (ABAS‐3, Adaptive Behavior Assessment System‐3) and psychopathology (ABCL, Adult Behavior Checklist). Caregivers were also asked to ensure that subjects wore an Actiwatch‐2 device continuously for at least ten days. From actograms, we calculated RAR amplitude, acrophase, robustness, intradaily variability (IV), interdaily stability (IS), and estimates of sleep quantity and timing. We compared these RAR metrics against those from (i) a previously published cohort of adults with epilepsy without ID (E–ID), and (ii) a historical control cohort of age‐ and sex‐matched intellectually able subjects from the Study of Latinos (SOL). Results 46 E + ID subjects (median age 26, 47% female) provided a median recording duration of 11 days. Surveys reflected low to extremely low levels of adaptive function and low/subclinical levels of psychopathology. Compared with E‐ID and SOL cohorts, E + ID subjects displayed significantly lower measures of RAR amplitude, robustness, and IS, with significantly higher IV and total daily sleep. K‐means clustering of E + ID subjects recognized a cluster with pronounced hypoactivity, hypersomnia, and elevated rhythm fragmentation (cluster A), an intermediate group with metrics similar to E‐ID, and cluster “C” subjects that featured hyper‐robust and high amplitude RARs. All three clusters were similar in age, body mass index, antiseizure medication (ASM) polytherapy, ABAS3, and ABCL scores. Significance Adults with epilepsy and intellectual disability display a wide spectrum of RAR phenotypes that do not neatly correlate with measures of adaptive function or epilepsy severity. Prospective studies are necessary to determine whether continuous actigraphic monitoring can sensitively capture changes in chronobiological health that may arise with disease progression, ASM side effects, or other acute health deteriorations. Plain Language Summary Rest‐activity rhythms (RARs) can be measured using continuously worn wrist activity monitors. We show that compared to controls, adults with epilepsy and intellectual disability (E + ID) display RARs that are more fragmented, weaker in amplitude, and unstable across days. Within our E + ID cohort, we observed a wide spectrum of RAR phenotypes that we clustered into three subtypes, which were similar in overall average measures of adaptive functioning and psychiatric symptoms.

Abstract Study Objectives Mobility restrictions imposed to suppress transmission of COVID-19 can alter physical activity (PA) and sleep patterns that are important for health and well-being. Characterization of response heterogeneity and their underlying associations may assist in stratifying the health impact of the pandemic. Methods We obtained wearable data covering baseline, incremental mobility restriction, and lockdown periods from 1,824 city-dwelling, working adults aged 21–40 years, incorporating 206,381 nights of sleep and 334,038 days of PA. Distinct rest-activity rhythm (RAR) profiles were identified using k-means clustering, indicating participants’ temporal distribution of step counts over the day. Hierarchical clustering of the proportion of days spent in each of these RAR profiles revealed four groups who expressed different mixtures of RAR profiles before and during the lockdown. Results Time in bed increased by 20 min during the lockdown without loss of sleep efficiency, while social jetlag measures decreased by 15 min. Resting heart rate declined by ~2 bpm. PA dropped an average of 42%. Four groups with different compositions of RAR profiles were found. Three were better able to maintain PA and weekday/weekend differentiation during lockdown. The least active group comprising ~51% of the sample, were younger and predominantly singles. Habitually less active already, this group showed the greatest reduction in PA during lockdown with little weekday/weekend differences. Conclusion In the early aftermath of COVID-19 mobility restriction, PA appears to be more severely affected than sleep. RAR evaluation uncovered heterogeneity of responses to lockdown that could associate with different outcomes should the resolution of COVID-19 be protracted.

The objective of this study is to investigate the association between actigraphic estimates of the sleep–wake rhythm and a range of functional domains that contribute to well-being in demented elderly patients. Eighty-seven women aged 85.5 ± 5.9 years (mean ± standard deviation) wore an actigraph for two weeks. Activity profiles were analyzed using nonparametric variables, including dichotomy indices, interdaily stability (IS), intradaily variability (IV), and relative amplitude (RA). The associations between these variables and cognitive, functional, behavioral, and emotional states (obtained from standardized neuropsychologic assessments and questionnaires administered to caregivers) were investigated by partial correlations and stepwise regressions. Cognitive, functional, behavioral, and emotional states showed medium to strong correlations with multiple rhythm variables. Partial correlations indicated that this could not be attributed to a uniform worsening with advancing cognitive decline. Stepwise regressions indicated three most distinctive rhythm variables: 1) the interdaily stability of the 24-hour rhythm was most strongly, negatively, related to cognitive decline and depression; 2) the median level of daytime activity was most strongly, negatively, related to impairments of function, of activities of daily living, and of social interaction; and 3) nocturnal restlessness was secondarily, positively, related to impairments of function and social interaction. Especially the interdaily stability and median daytime activity level, and secondarily nocturnal restlessness, showed a strong relationship with the functional status and well-being of demented elderly. This raises the possibility that treatments that enhance daytime activity and the stability of the rest–activity rhythm may improve well-being.

Abstract Study Objectives Disrupted daily rhythms are associated with mild cognitive impairment (MCI) and dementia. The specific nature of how rhythms and cognition are related, however, is unknown. We hypothesized characteristics from a nonparametric estimate of circadian rest-activity rhythm patterns would be associated to the development of MCI or dementia. Methods Wrist actigraphy from 1232 cognitively healthy, community-dwelling women (mean age 82.6 years) from the Study of Osteoporotic Fractures was used to estimate rest-activity patterns, including intradaily variability (IV), interdaily stability (IS), most active 10-hour period (M10), least active 5-hour period (L5), and relative amplitude (RA). Logistic regression examined associations of these predictors with 5-year incidence of MCI or dementia. Models were adjusted for potential confounders. Results Women with earlier sleep/wake times had higher risk of dementia, but not MCI, (early vs. average L5 midpoint: OR, 1.66; 95% CI, 1.08–2.55) as did women with smaller day/night activity differentials (low vs. high RA: OR, 1.96; 95% CI, 1.14–3.35). IV, IS, and M10 were not associated with MCI or dementia. Conclusion The timing and difference in day/night amplitude, but not variability of activity, may be useful as predictors of dementia.

Growing evidence highlights a tight interplay linking circadian and sleep–wake disturbances to the pathophysiology of neurodegenerative disorders. In α-synucleinopathies, three key points have emerged: (1) circadian and sleep–wake disruptions may increase the risk of neurodegeneration; (2) these alterations reflect widespread dysfunction in neural circuits regulating sleep, wakefulness, and biological rhythms; and (3) the prodromal condition of isolated rapid eye movement (REM) sleep behavior disorder (iRBD) offers a unique window into early pathological changes, as it is characterized by neurodegeneration in brainstem structures critical for sleep–wake regulation and REM sleep control. Hence, sleep- and circadian-related biomarkers may represent feasible tools for early diagnosis, prevention, and treatment across the spectrum of α-synucleinopathies. However, despite their potential, diagnostic or therapeutic pathways grounded in sleep and circadian biology have yet to be systematically explored or validated, and key questions remain, including the trajectories that characterize the clinical progression from iRBD to overt α-synucleinopathies. Key challenges include translational barriers, inter-individual variability in biomarker profiles, and the need for longitudinal studies to define clinically actionable thresholds. Against this backdrop, this mini-review synthesizes current evidence on sleep–wake rhythm alterations in iRBD as a prodromal stage of α-synucleinopathy-driven neurodegeneration. Candidate circadian biomarkers are discussed, including objective parameters from long-term actigraphic monitoring, encompassing rest–activity rhythms modeled with parametric and non-parametric approaches, as well as physiological indicators such as dim light melatonin onset and core body temperature.

Circadian rhythm disruptions are a common concern for poststroke patients undergoing rehabilitation and might negatively impact their functional outcomes. Our research aimed to uncover unique patterns and disruptions specific to poststroke rehabilitation patients and identify potential differences in specific rest-activity rhythm indicators when compared to inpatient controls with non-brain-related lesions, such as patients with spinal cord injuries. We obtained a 7-day recording with a wearable actigraphy device from 25 poststroke patients (n=9, 36% women; median age 56, IQR 46-71) and 25 age- and gender-matched inpatient control participants (n=15, 60% women; median age 57, IQR 46.5-68.5). To assess circadian rhythm, we used a nonparametric method to calculate key rest-activity rhythm indicators-relative amplitude, interdaily stability, and intradaily variability. Relative amplitude, quantifying rest-activity rhythm amplitude while considering daily variations and unbalanced amplitudes, was calculated as the ratio of the difference between the most active 10 continuous hours and the least active 5 continuous hours to the sum of these 10 and 5 continuous hours. We also examined the clinical correlations between rest-activity rhythm indicators and delirium screening tools, such as the 4 A's Test and the Barthel Index, which assess delirium and activities of daily living. Patients who had a stroke had higher least active 5-hour values compared to the control group (median 4.29, IQR 2.88-6.49 vs median 1.84, IQR 0.67-4.34; P=.008). The most active 10-hour values showed no significant differences between the groups (stroke group: median 38.92, IQR 14.60-40.87; control group: median 31.18, IQR 18.02-46.84; P=.93). The stroke group presented a lower relative amplitude compared to the control group (median 0.74, IQR 0.57-0.85 vs median 0.88, IQR 0.71-0.96; P=.009). Further analysis revealed no significant differences in other rest-activity rhythm metrics between the two groups. Among the patients who had a stroke, a negative correlation was observed between the 4 A's Test scores and relative amplitude (ρ=-0.41; P=.045). Across all participants, positive correlations emerged between the Barthel Index scores and both interdaily stability (ρ=0.34; P=.02) and the most active 10-hour value (ρ=0.42; P=.002). This study highlights the relevance of circadian rhythm disruptions in poststroke rehabilitation and provides insights into potential diagnostic and prognostic implications for rest-activity rhythm indicators as digital biomarkers.

Background Menopausal women undergo substantial physiological changes that can impact their overall health. Objectives We examined relationships between circadian rest-activity rhythms (CRARs) and multimorbidity progression in this population. Methods We used UK Biobank data, involving 10,138 participants, who were initially free of chronic conditions. We primarily focused on the relative amplitude (RA) of CRARs, tracking incident first chronic conditions (FCC), multimorbidity, and all-cause mortality. Multimorbidity was indicated by the presence of any 2/35 chronic conditions during the follow-up period. We used a multi-state model to assess the RA impact on the multimorbidity progression trajectory, encompassing transition from health to an FCC, to consequent multimorbidity, and ultimately to mortality, in parallel with sensitivity analyses to ensure results stability and reliability. Results During a mean 8.13-year follow-up period, we identified 855 incident multimorbidity cases and recorded 88 deaths. In a multi-state model, a lower RA was associated with an increased risk of transition from health to FCC onset [hazard ratio (HR): 1.18, 95% confidence interval (CI): 1.07–1.31] and also from an FCC to multimorbidity development (HR: 1.34, 95% CI: 1.12–1.61), even after adjusting for several confounding factors. Conclusions Among menopausal women, circadian rhythm disturbance increased the risk of transitioning from health to a single chronic condition, as well as transitioning from a single chronic condition to multimorbidity.

Background: Depression is a prevalent mental disorder that is undiagnosed and untreated in half of all cases. Wearable activity trackers collect fine-grained sensor data characterizing the behavior and physiology of users (ie, digital biomarkers), which could be used for timely, unobtrusive, and scalable depression screening. Objective: The aim of this study was to examine the predictive ability of digital biomarkers, based on sensor data from consumer-grade wearables, to detect risk of depression in a working population. Methods: This was a cross-sectional study of 290 healthy working adults. Participants wore Fitbit Charge 2 devices for 14 consecutive days and completed a health survey, including screening for depressive symptoms using the 9-item Patient Health Questionnaire (PHQ-9), at baseline and 2 weeks later. We extracted a range of known and novel digital biomarkers characterizing physical activity, sleep patterns, and circadian rhythms from wearables using steps, heart rate, energy expenditure, and sleep data. Associations between severity of depressive symptoms and digital biomarkers were examined with Spearman correlation and multiple regression analyses adjusted for potential confounders, including sociodemographic characteristics, alcohol consumption, smoking, self-rated health, subjective sleep characteristics, and loneliness. Supervised machine learning with statistically selected digital biomarkers was used to predict risk of depression (ie, symptom severity and screening status). We used varying cutoff scores from an acceptable PHQ-9 score range to define the depression group and different subsamples for classification, while the set of statistically selected digital biomarkers remained the same. For the performance evaluation, we used k-fold cross-validation and obtained accuracy measures from the holdout folds. Results: A total of 267 participants were included in the analysis. The mean age of the participants was 33 (SD 8.6, range 21-64) years. Out of 267 participants, there was a mild female bias displayed (n=170, 63.7%). The majority of the participants were Chinese (n=211, 79.0%), single (n=163, 61.0%), and had a university degree (n=238, 89.1%). We found that a greater severity of depressive symptoms was robustly associated with greater variation of nighttime heart rate between 2 AM and 4 AM and between 4 AM and 6 AM; it was also associated with lower regularity of weekday circadian rhythms based on steps and estimated with nonparametric measures of interdaily stability and autocorrelation as well as fewer steps-based daily peaks. Despite several reliable associations, our evidence showed limited ability of digital biomarkers to detect depression in the whole sample of working adults. However, in balanced and contrasted subsamples comprised of depressed and healthy participants with no risk of depression (ie, no or minimal depressive symptoms), the model achieved an accuracy of 80%, a sensitivity of 82%, and a specificity of 78% in detecting subjects at high risk of depression. Conclusions: Digital biomarkers that have been discovered and are based on behavioral and physiological data from consumer wearables could detect increased risk of depression and have the potential to assist in depression screening, yet current evidence shows limited predictive ability. Machine learning models combining these digital biomarkers could discriminate between individuals with a high risk of depression and individuals with no risk.

Background Rest-activity rhythm (RAR), a manifestation of circadian rhythms, has been associated with morbidity and mortality risk. However, RAR patterns in the general population and specifically the role of demographic characteristics in RAR pattern have not been comprehensively assessed. Therefore, we aimed to describe RAR patterns among non-institutionalized US adults and age, sex, and race/ethnicity variation using accelerometry data from a nationally representative population. Methods This cross-sectional study was conducted using the US National Health and Nutrition Examination Survey (NHANES) 2011–2014. Participants aged ≥20 years who were enrolled in the physical activity monitoring examination and had at least four 24-h periods of valid wrist accelerometer data were included in the present analysis. 24-h RAR metrics were generated using both extended cosinor model (amplitude, mesor, acrophase and pseudo-F statistic) and nonparametric methods (interdaily stability [IS] and intradaily variability [IV]). Multivariable linear regression was used to assess the association between RAR and age, sex, and race/ethnicity. Results Eight thousand two hundred participants (mean [SE] age, 49.1 [0.5] years) were included, of whom 52.2% were women and 67.3% Whites. Women had higher RAR amplitude and mesor, and also more robust (pseudo-F statistic), more stable (higher IS) and less fragmented (lower IV) RAR (all P trend  < 0.001) than men. Compared with younger adults (20–39 years), older adults (≥ 60 years) exhibited reduced RAR amplitude and mesor, but more stable and less fragmented RAR, and also reached their peak activity earlier (advanced acrophase) (all P trend  < 0.001). Relative to other racial/ethnic groups, Hispanics had the highest amplitude and mesor level, and most stable (highest IS) and least fragmented (lowest IV) RAR pattern ( P trend  < 0.001). Conversely, non-Hispanic blacks had the lowest peak activity level (lowest amplitude) and least stable (lowest IS) RAR pattern (all P trend  < 0.001). Conclusions In the general adult population, RAR patterns vary significantly according to sex, age and race/ethnicity. These results may reflect demographic-dependent differences in intrinsic circadian rhythms and may have important implications for understanding racial, ethnic, sex and other disparities in morbidity and mortality risk.