Multidimensional Sleep Profiles via Machine learning and Risk of Dementia and Cardiovascular Disease.

Importance: Sleep health comprises several dimensions such as duration and fragmentation of sleep, circadian activity, and daytime behavior. Yet, most research has focused on individual sleep characteristics. Studies are needed to identify sleep profiles incorporating multiple dimensions and to assess how different profiles may be linked to adverse health outcomes. Objective: To identify actigraphy-based 24-hour sleep/circadian profiles in older men and to investigate whether these profiles are associated with the incidence of dementia and cardiovascular disease (CVD) events over 12 years. Design: Data came from a prospective sleep study with participants recruited between 2003-2005 and followed until 2015-2016. Setting: Multicenter population-based cohort study. Participants: Among the 3,135 men enrolled, we excluded 331 men with missing or invalid actigraphy data and 137 with significant cognitive impairment at baseline, leading to a sample of 2,667 participants. Exposures: Leveraging 20 actigraphy-derived sleep and circadian activity rhythm variables, we determined sleep/circadian profiles using an unsupervised machine learning technique based on multiple coalesced generalized hyperbolic mixture modeling. Main Outcomes and Measures: Incidence of dementia and CVD events. Results: We identified three distinct sleep/circadian profiles: active healthy sleepers (AHS; n=1,707 (64.0%); characterized by normal sleep duration, higher sleep quality, stronger circadian rhythmicity, and higher activity during wake periods), fragmented poor sleepers (FPS; n=376 (14.1%); lower sleep quality, higher sleep fragmentation, shorter sleep duration, and weaker circadian rhythmicity), and long and frequent nappers (LFN; n=584 (21.9%); longer and more frequent naps, higher sleep quality, normal sleep duration, and more fragmented circadian rhythmicity). Over the 12-year follow-up, compared to AHS, FPS had increased risks of dementia and CVD events (Hazard Ratio (HR)=1.35, 95% confidence interval (CI)=1.02-1.78 and HR=1.32, 95% CI=1.08-1.60, respectively) after multivariable adjustment, whereas LFN showed a marginal association with increased CVD events risk (HR=1.16, 95% CI=0.98-1.37) but not with dementia (HR=1.09, 95%CI=0.86-1.38). Conclusion and Relevance: We identified three distinct multidimensional profiles of sleep health. Compared to healthy sleepers, older men with overall poor sleep and circadian activity rhythms exhibited worse incident cognitive and cardiovascular health. These results highlight potential targets for sleep interventions and the need for more comprehensive screening of poor sleepers for adverse outcomes. Key Points: Question: Are there distinct sleep/circadian profiles in older men, and if so, are they associated with the incidence of dementia and cardiovascular disease (CVD) events over 12 years?Findings: Three actigraphy-based profiles were identified: active healthy sleepers [AHS], fragmented poor sleepers [FPS], and long and frequent nappers [LFN]. Compared to AHS, FPS had increased risks of dementia and CVD events whereas LFN had marginal risk of CVD events.Meaning: Older men with distinct sleep/circadian profiles are at increased risk of incident dementia and CVD events, suggesting their potential as target populations for sleep interventions and screening for adverse outcomes.

Five-year changes in 24-hour sleep-wake activity and dementia risk in oldest old women.

INTRODUCTION: Sleep disruptions are associated with cognitive aging in older adults. However, it is unclear whether longitudinal changes in 24-hour multidimensional sleep-wake activity are linked to cognitive impairment in the oldest old. METHODS: We studied 733 cognitively unimpaired women (mean age=82.5±2.9 years) who completed two actigraphy assessments over five years. We performed hierarchical clustering on principal components in nine sleep, napping, and circadian rest-activity rhythm parameters to identify multidimensional sleep-wake change profiles and multinomial logistic regression to evaluate the associations between sleep-wake changes and risk of cognitive impairment at follow-up. RESULTS: We identified three sleep-wake change profiles: Stable Sleep (43.8%), Declining Nighttime Sleep (34.9%), and Increasing Sleepiness (21.3%). After adjustment for demographics and comorbidities, women with Increasing Sleepiness had approximately doubled (odds ratio=2.21, p=0.018) risk of dementia compared to those with Stable Sleep. DISCUSSION: Increasing sleepiness may be an independent marker or risk factor for dementia in oldest old women.

Power-law properties of nocturnal arrhythmia avalanches: A novel marker for incident cardiovascular events.

BACKGROUND: Bursting nonsustained cardiac arrhythmia events are a common observation during sleep. OBJECTIVES: The purpose of this study was to investigate the hypothesis that nocturnal arrhythmia episode durations could follow a power law, whose exponent could predict long-term clinical outcomes. METHODS: We defined "nocturnal arrhythmia avalanche" (NAA) as any instance of a drop in electrocardiographic (ECG) template-matched R-R intervals ≥30% of R-R baseline, followed by a return to 90% of baseline. We studied NAA in ECG recordings obtained from the Sleep Heart Health Study (SHHS), Osteoporotic Fractures in Men Study (MrOS) Study, and Multi-Ethnic Study of Atherosclerosis (MESA). The association of nocturnal arrhythmia durations with a power-law distribution was evaluated and the association of derived power-law exponents (α) with major adverse cardiovascular (CV) events and mortality assessed with multivariable Cox regression. RESULTS: A total of 9176 participants were studied. NAA episodes distribution was consistent with power-law vs comparator distributions in all datasets studied (positive log likelihood ratio of power-law vs exponential in MESA: 83%; SHHS: 69%; MrOS: 81%; power-law vs log-normal in MESA: 95%; SHHS: 35%; MrOS: 64%). The NAA power-law exponent (α) showed a significant association of with adverse CV outcomes (association with CV mortality: SHHS hazard ratio 1.39 [1.07-1.79], P = .012; MrOS hazard ratio 1.42 [1.02-1.94], P = .039; association with CV events: MESA HR 3.46 [1.46-8.21], P = .005) in multivariable Cox regression, after adjusting for conventional CV risk factors and nocturnal ectopic rate. CONCLUSION: The NAA power-law exponent is a reproducible, predictive marker for incident CV events and mortality.

Endometrial compaction to predict pregnancy outcomes in patients undergoing assisted reproductive technologies: a systematic review and meta-analysis.

STUDY QUESTION: Does endometrial compaction (EC) help predict pregnancy outcomes in those undergoing ART? SUMMARY ANSWER: EC is associated with a significantly higher clinical pregnancy rate (CPR) and ongoing pregnancy rate (OPR), but this does not translate to live birth rate (LBR). WHAT IS KNOWN ALREADY: EC describes the progesterone-induced decrease in endometrial thickness, which may be observed following the end of the proliferative phase, prior to embryo transfer. EC is proposed as a non-invasive tool to help predict pregnancy outcome in those undergoing ART, however, published data is conflicting. STUDY DESIGN SIZE DURATION: A literature search was carried out by two independent authors using PubMed, Cochrane Library, MEDLINE, Embase, Science Direct, Scopus, and Web of Science from inception of databases to May 2023. All peer-reviewed studies reporting EC and pregnancy outcomes in patients undergoing IVF/ICSI treatment were included. PARTICIPANTS/MATERIALS SETTING METHODS: The primary outcome is LBR. Secondary outcomes included other pregnancy metrics (positive pregnancy test (PPT), CPR, OPR, miscarriage rate (MR)) and rate of EC. Comparative meta-analyses comparing EC and no EC were conducted for each outcome using a random-effects model if I 2 > 50%. The Mantel-Haenszel method was applied for pooling dichotomous data. Results are presented as odds ratios (OR) with 95% CI. MAIN RESULTS AND THE ROLE OF CHANCE: Out of 4030 screened articles, 21 cohort studies were included in the final analysis (n = 27 857). No significant difference was found between LBR in the EC versus the no EC group (OR 0.95; 95% CI 0.87-1.04). OPR was significantly higher within the EC group (OR 1.61; 95% CI 1.09-2.38), particularly when EC ≥ 15% compared to no EC (OR 3.52; 95% CI 2.36-5.23). CPR was inconsistently defined across the studies, affecting the findings. When defined as a viable intrauterine pregnancy <12 weeks, the EC group had significantly higher CPR than no EC (OR 1.83; 95% CI 1.15-2.92). No significant differences were found between EC and no EC for PPT (OR 1.54; 95% CI 0.97-2.45) or MR (OR 1.06; 95% CI 0.92-1.56). The pooled weighted incidence of EC across all studies was 32% (95% CI 26-38%). LIMITATIONS REASONS FOR CAUTION: Heterogeneity due to differences between reported pregnancy outcomes, definition of EC, method of ultrasound, and cycle protocol may account for the lack of translation between CPR/OPR and LBR findings; thus, all pooled data should be viewed with an element of caution. WIDER IMPLICATIONS OF THE FINDINGS: In this dataset, the significantly higher CPR/OPR with EC does not translate to LBR. Although stratification of women according to EC cannot currently be recommended in clinical practice, a large and well-designed clinical trial to rigorously assess EC as a non-invasive predictor of a successful pregnancy is warranted. We urge for consistent outcome reporting to be mandated for ART trials so that data can be pooled, compared, and concluded on. STUDY FUNDING/COMPETING INTERESTS: H.A. was supported by the Hewitt Fertility Centre. S.G.P. and J.W. were supported by the Liverpool University Hospital NHS Foundation Trust. D.K.H. was supported by a Wellbeing of Women project grant (RG2137) and MRC clinical research training fellowship (MR/V007238/1). N.T. was supported by the National Institute for Health and Care Research. D.K.H. had received honoraria for consultancy for Theramex and has received payment for presentations from Theramex and Gideon Richter. The remaining authors have no conflicts of interest to report. REGISTRATION NUMBER: PROSPERO CRD42022378464.

From sleep patterns to heart rhythm: Predicting atrial fibrillation from overnight polysomnograms.

BACKGROUND: Atrial fibrillation (AF) is often asymptomatic and thus under-observed. Given the high risks of stroke and heart failure among patients with AF, early prediction and effective management are crucial. Given the prevalence of obstructive sleep apnea among AF patients, electrocardiogram (ECG) analysis from polysomnography (PSG) offers a unique opportunity for early AF prediction. Our aim is to identify individuals at high risk of AF development from single­lead ECGs during standard PSG. METHODS: We analyzed 18,782 single­lead ECG recordings from 13,609 subjects undergoing PSG at the Massachusetts General Hospital sleep laboratory. AF presence was identified using ICD-9/10 codes. The dataset included 15,913 recordings without AF history and 2054 recordings from patients diagnosed with AF between one month to fifteen years post-PSG. Data were partitioned into training, validation, and test cohorts ensuring that individual patients remained exclusive to each cohort. The test set was held out during the training process. We employed two different methods for feature extraction to build a final model for AF prediction: Extraction of hand-crafted ECG features and a deep learning method. For extraction of ECG-hand-crafted features, recordings were split into 30-s windows, and those with a signal quality index (SQI) below 0.95 were discarded. From each remaining window, 150 features were extracted from the time, frequency, time-frequency domains, and phase-space reconstructions of the ECG. A compilation of 12 statistical features summarized these window-specific features per recording, resulting in 1800 features (12 × 150). A pre-trained deep neural network from the PhysioNet Challenge 2021 was updated using transfer learning to discriminate recordings with and without AF. The model processed PSG ECGs in 16-s windows to generate AF probabilities, from which 13 statistical features were extracted. Combining 1800 features from feature extraction with 13 from the deep learning model, we performed a feature selection and subsequently trained a shallow neural network to predict future AF and evaluated its performance on the test cohort. RESULTS: On the test set, our model exhibited sensitivity, specificity, and precision of 0.67, 0.81, and 0.3, respectively, for AF prediction. Survival analysis revealed a hazard ratio of 8.36 (p-value: 1.93 × 10-52) for AF outcomes using the log-rank test. CONCLUSIONS: Our proposed ECG analysis method, utilizing overnight PSG data, shows promise in AF prediction despite modest precision, suggesting false positives. This approach could enable low-cost screening and proactive treatment for high-risk patients. Refinements, including additional physiological parameters, may reduce false positives, enhancing clinical utility and accuracy.

Long-term Trajectories of Low Back Pain in Older Men: A Prospective Cohort Study With 10-Year Analysis of the Osteoporotic Fractures in Men Study.

Although low back pain (LBP) may persist or recur over time, few studies have evaluated the individual course of LBP over a long-term period, particularly among older adults. Based on data from the longitudinal Osteoporotic Fractures in Men (MrOS) Study, we aimed to identify and describe different LBP trajectories in older men and characterize members in each trajectory group. A total of 5 976 community-dwelling men (mean age = 74.2) enrolled at 6 U.S. sites were analyzed. Participants self-reported LBP (yes/no) every 4 months for a maximum of 10 years. Latent class growth modeling was performed to identify unique LBP trajectory groups that explained variation in the LBP data. The association of baseline characteristics with trajectory group membership was assessed using univariable and multivariable multinominal logistic regression. A 5-class solution was chosen; no/rare LBP (n = 2 442/40.9%), low frequency-stable LBP (n = 1 040/17.4%), low frequency-increasing LBP (n = 719/12%), moderate frequency-decreasing LBP (n = 745/12.5%), and high frequency-stable LBP (n = 1 030/17.2%). History of falls (OR = 1.52), history of LBP (OR = 6.37), higher physical impairment (OR = 1.51-2.85), and worse psychological function (OR = 1.41-1.62) at baseline were all associated with worse LBP trajectory groups in this sample of older men. These findings present an opportunity for targeted interventions and/or management to older men with worse or increasing LBP trajectories and associated modifiable risk factors to reduce the impact of LBP and improve quality of life.

Associations between Longitudinal Changes in Sleep Stages and Risk of Cognitive Decline in Older Men.

STUDY OBJECTIVES: To investigate the relationships between longitudinal changes in sleep stages and the risk of cognitive decline in older men. METHODS: This study included 978 community-dwelling older men who participated in the first (2003-2005) and second (2009-2012) sleep ancillary study visits of the Osteoporotic Fractures in Men Study. We examined the longitudinal changes in sleep stages at the initial and follow-up visits, and the association with concurrent clinically relevant cognitive decline during the 6.5-year follow-up. RESULTS: Men with low to moderate (quartile 2, Q2) and moderate increase (Q3) in N1 sleep percentage had a reduced risk of cognitive decline on the Modified Mini-Mental State Examination compared to those with a substantial increase (Q4) in N1 sleep percentage. Additionally, men who experienced a low to moderate (Q2) increase in N1 sleep percentage had a lower risk of cognitive decline on the Trails B compared with men in the reference group (Q4). Furthermore, men with the most pronounced reduction (Q1) in N2 sleep percentage had a significantly higher risk of cognitive decline on the Trails B compared to those in the reference group (Q4). No significant association was found between changes in N3 and rapid eye movement sleep and the risk of cognitive decline. CONCLUSIONS: Our results suggested that a relatively lower increase in N1 sleep showed a reduced risk of cognitive decline. However, a pronounced decrease in N2 sleep was associated with concurrent cognitive decline. These findings may help identify older men at risk of clinically relevant cognitive decline.

Psoriasis and Sleep Disturbance: A US Population-Based Study Using the NHANES Database.

INTRODUCTION: Psoriasis, a chronic inflammatory skin condition, affects approximately 3.0% of the US population, with patients often experiencing significant sleep disturbances. These disturbances include a higher prevalence of conditions such as obstructive sleep apnea, restless leg syndrome, and insomnia. Given the additional risks for cardiovascular disease, metabolic disorders, and depression linked to both poor sleep and psoriasis, addressing sleep issues in this patient group is critical. METHODS: The study utilized National Health and Nutrition Examination Survey (NHANES) data, focusing on individuals aged ≥ 20 years who provided information on psoriasis status and sleep. Multistage stratified survey methodology was applied, with multivariable logistic regression models used to examine the association between psoriasis and sleep issues, adjusting for factors such as age, gender, and health history. RESULTS: Psoriasis diagnosis was significantly associated with trouble sleeping (adjusted odds ratio [aOR] 1.88; 95% confidence interval [CI] 1.44-2.45). There was no significant association between psoriasis and sleep quantity. Older age, female gender, and a history of sleep disorders were predictors of trouble sleeping among psoriasis patients. CONCLUSIONS: Psoriasis is significantly associated with sleep disturbances, independent of sleep duration. This underscores the need for clinical screening focusing on sleep quality rather than quantity in psoriasis patients to effectively identify and treat sleep-related comorbidities. Further research using objective sleep measures is warranted to guide clinical management and improve patient quality of life.

From Sleep Patterns to Heart Rhythms: Predicting Atrial Fibrillation from Overnight Polysomnograms.

Background: Atrial fibrillation (AF) is often asymptomatic and thus under-observed. Given the high risks of stroke and heart failure among patients with AF, early prediction and effective management are crucial. Importantly, obstructive sleep apnea is highly prevalent among AF patients (60-90%); therefore, electrocardiogram (ECG) analysis from polysomnography (PSG), a standard diagnostic tool for subjects with suspected sleep apnea, presents a unique opportunity for the early prediction of AF. Our goal is to identify individuals at a high risk of developing AF in the future from a single-lead ECG recorded during standard PSGs. Methods: We analyzed 18,782 single-lead ECG recordings from 13,609 subjects at Massachusetts General Hospital, identifying AF presence using ICD-9/10 codes in medical records. Our dataset comprises 15,913 recordings without a medical record for AF and 2,056 recordings from patients who were first diagnosed with AF between 1 day to 15 years after the PSG recording. The PSG data were partitioned into training, validation, and test cohorts. In the first phase, a signal quality index (SQI) was calculated in 30-second windows and those with SQI < 0.95 were removed. From each remaining window, 150 hand-crafted features were extracted from time, frequency, time-frequency domains, and phase-space reconstructions of the ECG. A compilation of 12 statistical features summarized these window-specific features per recording, resulting in 1,800 features. We then updated a pre-trained deep neural network and data from the PhysioNet Challenge 2021 using transfer-learning to discriminate between recordings with and without AF using the same Challenge data. The model was applied to the PSG ECGs in 16-second windows to generate the probability of AF for each window. From the resultant probability sequence, 13 statistical features were extracted. Subsequently, we trained a shallow neural network to predict future AF using the extracted ECG and probability features. Results: On the test set, our model demonstrated a sensitivity of 0.67, specificity of 0.81, and precision of 0.3 for predicting AF. Further, survival analysis for AF outcomes, using the log-rank test, revealed a hazard ratio of 8.36 (p-value of 1.93 × 10 -52 ). Conclusions: Our proposed ECG analysis method, utilizing overnight PSG data, shows promise in AF prediction despite a modest precision indicating the presence of false positive cases. This approach could potentially enable low-cost screening and proactive treatment for high-risk patients. Ongoing refinement, such as integrating additional physiological parameters could significantly reduce false positives, enhancing its clinical utility and accuracy.

Pioneering a Multi-Phase Framework to Harmonize Self-Reported Sleep Data Across Cohorts.

STUDY OBJECTIVES: Harmonizing and aggregating data across studies enable pooled analyses that support external validation and enhance replicability and generalizability. However, the multidimensional nature of sleep poses challenges for data harmonization and aggregation. Here we describe and implement our process for harmonizing self-reported sleep data. METHODS: We established a multi-phase framework to harmonize self-reported sleep data: (1) compile items; (2) group items into domains; (3) harmonize items; and (4) evaluate harmonizability. We applied this process to produce a pooled multi-cohort sample of five United States cohorts plus a separate yet fully harmonized sample from Rotterdam, Netherlands. Sleep and sociodemographic data are described and compared to demonstrate the utility of harmonization and aggregation. RESULTS: We collected 190 unique self-reported sleep items and grouped them into 15 conceptual domains. Using these domains as guiderails, we developed 14 harmonized items measuring aspects of Satisfaction, Alertness/Sleepiness, Timing, Efficiency, Duration, Insomnia, and Sleep Apnea. External raters determined that 13 of these 14 items had moderate-to-high harmonizability. Alertness/Sleepiness items had lower harmonizability, while continuous, quantitative items (e.g., timing, total sleep time, efficiency) had higher harmonizability. Descriptive statistics identified features that are more consistent (e.g., wake-up time, duration) and more heterogeneous (e.g., time in bed, bedtime) across samples. CONCLUSIONS: Our process can guide researchers and cohort stewards towards effective sleep harmonization and provides a foundation for further methodological development in this expanding field. Broader national and international initiatives promoting common data elements across cohorts are needed to enhance future harmonization and aggregation efforts.

Association of back pain with all-cause and cause-specific mortality among older men: a cohort study.

OBJECTIVE: We evaluated whether more severe back pain phenotypes-persistent, frequent, or disabling back pain-are associated with higher mortality rate among older men. METHODS: In this secondary analysis of a prospective cohort, the Osteoporotic Fractures in Men (MrOS) study, we evaluated mortality rates by back pain phenotype among 5215 older community-dwelling men (mean age, 73 years, SD = 5.6) from 6 sites in the United States. The primary back pain measure used baseline and Year 5 back pain questionnaire data to characterize participants as having no back pain, nonpersistent back pain, infrequent persistent back pain, or frequent persistent back pain. Secondary measures of back pain from the Year 5 questionnaire included disabling back pain phenotypes. The main outcomes measured were all-cause and cause-specific death. RESULTS: After the Year 5 exam, during up to 18 years of follow-up (mean follow-up = 10.3 years), there were 3513 deaths (1218 cardiovascular, 764 cancer, 1531 other). A higher proportion of men with frequent persistent back pain versus no back pain died (78% versus 69%; sociodemographic-adjusted HR = 1.27, 95% CI = 1.11-1.45). No association was evident after further adjustment for health-related factors, such as self-reported general health and comorbid chronic health conditions (fully adjusted HR = 1.00; 95% CI = 0.86-1.15). Results were similar for cardiovascular deaths and other deaths, but we observed no association of back pain with cancer deaths. Secondary back pain measures, including back-related disability, were associated with increased mortality risk that remained statistically significant in fully adjusted models. CONCLUSION: Although frequent persistent back pain was not independently associated with risk of death in older men, additional secondary disabling back pain phenotypes were independently associated with increased mortality rate. Future investigations should evaluate whether improvements in disabling back pain affect general health and well-being or risk of death.

Sleep-wake behaviors associated with cognitive performance in middle-aged participants of the Hispanic Community Health Study/Study of Latinos.

OBJECTIVES: Many sleep-wake behaviors have been associated with cognition. We examined a panel of sleep-wake/activity characteristics to determine which are most robustly related to having low cognitive performance in midlife. Secondarily, we evaluate the predictive utility of sleep-wake measures to screen for low cognitive performance. METHODS: The outcome was low cognitive performance defined as being >1 standard deviation below average age/sex/education internally normalized composite cognitive performance levels assessed in the Hispanic Community Health Study/Study of Latinos. Analyses included 1006 individuals who had sufficient sleep-wake measurements about 2years later (mean age=54.9, standard deviation= 5.1; 68.82% female). We evaluated associations of 31 sleep-wake variables with low cognitive performance using separate logistic regressions. RESULTS: In individual models, the strongest sleep-wake correlates of low cognitive performance were measures of weaker and unstable 24-hour rhythms; greater 24-hour fragmentation; longer time-in-bed; and lower rhythm amplitude. One standard deviation worse on these sleep-wake factors was associated with ∼20%-30% greater odds of having low cognitive performance. In an internally cross-validated prediction model, the independent correlates of low cognitive performance were: lower Sleep Regularity Index scores; lower pseudo-F statistics (modellability of 24-hour rhythms); lower activity rhythm amplitude; and greater time in bed. Area under the curve was low/moderate (64%) indicating poor predictive utility. CONCLUSION: The strongest sleep-wake behavioral correlates of low cognitive performance were measures of longer time-in-bed and irregular/weak rhythms. These sleep-wake assessments were not useful to identify previous low cognitive performance. Given their potential modifiability, experimental trials could test if targeting midlife time-in-bed and/or irregular rhythms influences cognition.

Examining the predictive validity of a managerial coaching scale: a longitudinal study.

Managerial coaching remains a widespread and popular organizational development intervention applied across numerous industries to enhance critical workplace outcomes and employee attitudes, yet no studies to date have evaluated the temporal precedence within these relationships. This study sought to assess the predictive validity of the widely used Employee Perceptions of Supervisor/Line Manager Coaching Behavior Measure managerial coaching scale (CBI), employing a longitudinal design and following the testing of the causal hypothesized relationship framework. Three hypotheses were evaluated using three commonly associated variables with managerial coaching (role clarity, job satisfaction, and organization commitment), using longitudinal data collected over two waves from full-time US employees (n = 313). The study followed a two-wave design, collecting data over two time points to test for longitudinal measurement invariance and three reciprocal cross-lagged models. Results detected statistically significant cross-lagged and reciprocal cross-lagged effects in the role clarity and organization commitment models, highlighting a reciprocal relationship between managerial coaching behaviors and the two variables. However, only the reciprocal cross-lagged effect was statistically significant in the job satisfaction model. Findings suggest the predictive validity of the CBI scale for role clarity and organization commitment. Moreover, results indicate employee attitudes influenced managerial coaching behaviors over time across all three models, emphasizing the potential impact of employee attitudes on leadership effectiveness. This study highlights the complex relationships between managerial coaching and workplace outcomes, offering nuanced insights for improved understanding.

Association of Disrupted Delta Wave Activity During Sleep With Long-Term Cardiovascular Disease and Mortality.

BACKGROUND: Delta wave activity is a prominent feature of deep sleep, which is significantly associated with sleep quality. OBJECTIVES: The authors hypothesized that delta wave activity disruption during sleep could predict long-term cardiovascular disease (CVD) and CVD mortality risk. METHODS: The authors used a comprehensive power spectral entropy-based method to assess delta wave activity during sleep based on overnight polysomnograms in 4,058 participants in the SHHS (Sleep Heart Health Study) and 2,193 participants in the MrOS (Osteoporotic Fractures in Men Study) Sleep study. RESULTS: During 11.0 ± 2.8 years of follow-up in SHHS, 729 participants had incident CVD and 192 participants died due to CVD. During 15.5 ± 4.4 years of follow-up in MrOS, 547 participants had incident CVD, and 391 died due to CVD. In multivariable Cox regression models, lower delta wave entropy during sleep was associated with higher risk of coronary heart disease (SHHS: HR: 1.46; 95% CI: 1.02-2.06; P = 0.03; MrOS: HR: 1.79; 95% CI: 1.17-2.73; P < 0.01), CVD (SHHS: HR: 1.60; 95% CI: 1.21-2.11; P < 0.01; MrOS: HR: 1.43; 95% CI: 1.00-2.05; P = 0.05), and CVD mortality (SHHS: HR: 1.94; 95% CI: 1.18-3.18; P < 0.01; MrOS: HR: 1.66; 95% CI: 1.12-2.47; P = 0.01) after adjusting for covariates. The Shapley Additive Explanations method indicates that low delta wave entropy was more predictive of coronary heart disease, CVD, and CVD mortality risks than conventional sleep parameters. CONCLUSIONS: The results suggest that delta wave activity disruption during sleep may be a useful metric to identify those at increased risk for CVD and CVD mortality.

Sleep Duration Polygenic Risk and Phenotype: Associations with Biomarkers of Accelerated Aging in the Baltimore Longitudinal Study of Aging.

We sought to explore whether genetic risk for, and self-reported, short sleep are associated with biological aging and whether age and sex moderate these associations. Participants were a subset of individuals from the Baltimore Longitudinal Study of Aging who had complete data on self-reported sleep (n = 567) or genotype (n = 367). Outcomes included: Intrinsic Horvath age, Hannum age, PhenoAge, GrimAge, and DNAm-based estimates of plasminogen activator inhibitor-1 (PAI-1) and granulocyte count. Results demonstrated that polygenic risk for short sleep was positively associated with granulocyte count; compared to those reporting <6 hr sleep, those reporting >7 hr demonstrated faster PhenoAge and GrimAge acceleration and higher estimated PAI-1. Polygenic risk for short sleep and self-reported sleep duration interacted with age and sex in their associations with some of the outcomes. Findings highlight that polygenic risk for short sleep and self-reported long sleep is associated with variation in the epigenetic landscape and subsequently aging.

Stressful life events and low back pain in older men: A cross-sectional and prospective analysis using data from the MrOS study.

BACKGROUND: Stressful life events, such as loss of a partner, loss of a pet or financial problems, are more common with increasing age and may impact the experience of pain. The aim of the current study is to determine the cross-sectional and prospective association between stressful life events and low back pain reporting in the Osteoporotic Fracture in Men Study, a cohort of older men aged ≥65 years. METHODS: At a study visit (March 2005-May 2006), 5149 men reported whether they had experienced a stressful life event or low back pain in the prior 12 months. Following that visit, data on low back pain patients were gathered through triannual questionnaires every 4 months for 1 year. Multivariable logistic regression analyses estimated the association of stressful life events with recent past low back pain or future low back pain. RESULTS: N = 2930, (57%) men reported at least one stressful life event. The presence of a stressful life event was associated with greater odds of any low back pain (OR = 1.42 [1.26-1.59]) and activity-limiting low back pain (OR = 1.74 [1.50-2.01]) in the same period and of any low back pain (OR = 1.56 [1.39-1.74]) and frequent low back pain (OR = 1.80 [1.55-2.08]) in the following year. CONCLUSION: In this cohort of men, the presence of stressful life events increased the likelihood of reporting past and future low back pain. SIGNIFICANCE: Stressful life events such as accident or illness to a partner are common in later life and may impact the experience of pain. We present cross-sectional and prospective data highlighting a consistent association between stressful life events and low back pain in older men. Further, there is evidence to suggest that this relationship is upregulated by an individual's living situation. This information may be used to strengthen a biopsychosocial perspective of an individual's pain experience.

Accelerometer-assessed sleep and decline in physical function in older men.

OBJECTIVES: Assess the prospective association of actigraphically measured sleep with self-report and objective measures of physical function among community-dwelling older men. METHODS: Participants were (n = 1496) men aged ≥65 years from the Osteoporotic Fractures in Men Study and ancillary sleep study who were followed up at 4 years for physical function outcomes. Sleep predictors included baseline total sleep time (<6, 6-8 hours [reference], >8 hours), sleep efficiency (<80% or ≥80% [reference]), wake after sleep onset (<90 [reference] or ≥90 minutes), and sleep onset latency (<30 [reference] or ≥30 minutes), measured by wrist actigraphy. Outcomes included self-reported difficulties in mobility and instrumental activities of daily living and objective measures of physical performance (time to complete chair stands, gait speed, grip strength, best narrow walk pace). Multivariable regression models estimated associations between the sleep predictors and change in physical function at follow-up, adjusting for demographic and health-related variables. RESULTS: Participants with short average baseline total sleep time (<6 hours) had significantly greater slowing in their walking speed from baseline to follow-up. Participants with long baseline sleep onset latency (≥30 minutes) had significant increases in mobility difficulties and time to complete chair stands. Sleep efficiency and wake after sleep onset were not significantly associated with any outcomes. No sleep predictors were associated with change in instrumental activities of daily living. CONCLUSIONS: These findings add to the body of evidence showing links between poor sleep and subsequent declines in physical function. Further experimental research is needed to understand the mechanisms at play.

Multidimensional Sleep Health and Long-Term Cognitive Decline in Community-Dwelling Older Men.

Specific sleep characteristics have been associated with cognitive decline, Alzheimer's disease, and related dementias; however, studies examining the association between multidimensional sleep (a more comprehensive integration of sleep parameters) and cognitive decline are lacking. Among 2,811 older men without dementia, those with none, 1-2, and 3-5 "poor" self-reported sleep health dimensions had an adjusted 10-year change score of global cognition (3MS) of 2.9, 4.0 and 3.5 points (p-trend = 0.05), and in executive function (Trails B) completion time of 36.7, 42.7, and 46.7 seconds (p-trend < 0.01), respectively. In conclusion, a multidimensional measure of sleep health was associated with greater cognitive decline.