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Objectives: Environmental risk factors may contribute to sleep-disordered breathing. We investigated the association between indoor particulate matter ≤2.5µm in aerodynamic diameter (PM 2.5 ) and sleep-disordered breathing in children in an urban US community., Methods: The sample consisted of children aged 6-12years living in predominantly low-income neighborhoods in Boston, Massachusetts. Indoor PM 2.5 was measured in participants' main living areas for 7days using the Environmental Multipollutant Monitoring Assembly device. High indoor PM 2.5 exposure was defined as greater than the sample weekly average 80th percentile level (≥15.6 μg/m 3 ). Sleep-disordered breathing was defined as an Apnea-Hypopnea-Index (AHI) or Oxygen-Desaturation-Index (ODI) (≥3% desaturation) of ≥5 events/hour. Habitual loud snoring was defined as caregiver-report of loud snoring (most or all the time each week) over the past 4weeks. We examined the associations of PM 2.5 with sleep-disordered breathing or snoring using logistic regression adjusting for potential confounders., Results: The sample included 260 children (mean age 9.6years; 41% female), with 32% (n = 76) classified as having sleep-disordered breathing. In a logistic regression model adjusted for socioeconomics and seasonality, children exposed to high indoor PM 2.5 levels (n = 53) had a 3.53-fold increased odds for sleep-disordered breathing (95%CI: 1.57, 8.11, p = .002) compared to those with lower indoor PM 2.5 . This association persisted after additional adjustments for physical activity, outdoor PM 2.5 , environmental tobacco smoke, and health characteristics. Similar associations were observed for snoring and indoor PM 2.5. CONCLUSIONS: Children with higher indoor PM 2.5 exposure had greater odds of sleep-disordered breathing and habitual loud snoring, suggesting that indoor air quality contributes to sleep disparities., Competing Interests: Declaration of conflicts of interest S. Redline has consulted for Eli Lilly Inc (unrelated to this project). W. Phipatanakul has consulted for Genentech, Novartis, Sanofi, Regeneron, GSK, Astra Zeneca for asthma-related therapeutics, unrelated to this project. All other authors declare no conflicts of interest., (Copyright © 2024 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2024 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.)

Rationale: Comorbid insomnia and sleep apnea is reported to have worse outcomes than either condition alone. The local genetic correlations of these disorders are unknown., Objectives: To identify local genomic regions with heritability for clinically diagnosed sleep apnea and insomnia, and to identify local genetic correlations between these disorders and/or hypersomnia., Methods: Fifty thousand two hundred seventeen patients of European ancestry were examined. Global and local heritability and genetic correlations for independent regions were calculated, adjusting for obesity and other covariates., Results: Sleep apnea and insomnia were significantly globally heritable and had 118 and 168 genetic regions with local heritability p-values <.05, respectively. One region had a significant genetic correlation for sleep apnea and hypersomnia (p-value = 9.85 × 10 -4 )., Conclusions: Clinically diagnosed sleep apnea and insomnia have minimal shared genetic architecture, supporting genetically distinct comorbid insomnia and sleep apnea components. However, additional correlated regions may be identified with additional sample size and methodological improvements., (Copyright © 2024 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.)

Objectives: Examine the associations of sleep measures with kidney function changes over time among individuals from a community-based study., Methods: The sample includes 1657 participants (287 with chronic kidney disease [CKD]) in the Multi-Ethnic Study of Atherosclerosis Sleep Cohort (mean age: 57.7 years, male: 46.0%). We examined associations between a large set of sleep variables (polysomnography, actigraphy, and questionnaires) and cardiovascular disease risk factors and changes in estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio over approximately 5 years using high-dimensional regression. We investigated the modifying effect of sleep on the associations between cardiovascular disease risk factors and kidney function., Results: Sleep metrics predicted kidney function decline only among individuals with baseline CKD. Among this group, eGFR decline was associated with decreased stage N3 sleep (0.32 mL/min/1.73 m 2 /y per 10% decrease in N3, p < .001); increased actigraphy napping frequency (beta: -0.20 [-0.30, -0.07]); and actigraphy sleep midpoint trajectory in early morning (ref: midnight, beta: -0.84 [-1.19, -0.50]). Urinary albumin-to-creatinine ratio increase was associated with high wake bouts trajectory (ref: low, beta: 0.97 [0.28, 1.67]) and increased sleep-related hypoxemia (oxygen saturation %time<90 [≥5%], beta: 2.17 [1.26, 3.08]). Sleep metrics--N3 sleep, naps, and midpoint trajectory--significantly modified associations between hemoglobin A1C and eGFR decline., Conclusions: Reduced deep sleep, daytime napping, increased wake bouts, delayed sleep rhythms, and overnight hypoxemia are associated with longitudinal kidney function decline, with effects most apparent in individuals with CKD. Deep sleep, napping, and sleep timing modified the association between hemoglobin A1C and kidney function., (Copyright © 2022 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.)

New sleep technologies have become pervasive in the consumer space, and are becoming highly common in research and clinical sleep settings. The rapid, widespread use of largely unregulated and unstandardized technology has enabled the quantification of many different facets of sleep health, driving scientific discovery. As sleep scientists, it is our responsibility to inform principles and practices for proper evaluation of any new technology used in the clinical and research settings, and by consumers. A current lack of standardized methods for evaluating technology performance challenges the rigor of our scientific methods for accurate representation of the sleep health facets of interest. This special article describes the rationale and priorities of an interdisciplinary effort for rigorous, standardized, and rapid performance evaluation (previously, "validation") of new sleep and sleep disorders related technologies of all kinds (eg, devices or algorithms), including an associated article template for a new initiative for publication in Sleep Health of empirical studies systematically evaluating the performance of new sleep technologies. A structured article type should streamline manuscript development and enable more rapid writing, review, and publication. The goal is to promote rapid and rigorous evaluation and dissemination of new sleep technology, to enhance sleep research integrity, and to standardize terminology used in Rigorous Performance Evaluation papers to prevent misinterpretation while facilitating comparisons across technologies., Competing Interests: Funding & declaration of conflicts of interest MdZ has received funding from Noctrix Health, Inc., Lisa Health Inc., Verily Life Sciences LLC., the National Science Foundation (NSF) IIP-2111818; he has patents issued: US222921894, US20140316191, and pending: EP3591659, JP2020014841, US20200013511, US20200222699, AU2018302101, EP3654839, WO2019018400, WO2022006119 WO2022032121. MG received grants from Kemin Foods, Jazz Pharmaceuticals, and CeraZ; he has received consulting fees from Idorsia Pharmaceuticals, Fitbit, Smartypants Vitamins, Natrol, National Sleep Foundation, American Sleep Medicine Foundation; He has received publishing royalties from Elsevier; he has received speaking honoraria and/or travel support from New York University, University of Maryland, Stavis & Cohen, University of Miami, Clinical Education Alliance, and Jazz Pharmaceuticals. SR received consulting fees from Jazz Pharma and Eli Lilly, and has received consulting fees for serving as a member of the Scientific Advisory Board for Apnimed, Inc. Outside of the current work, OMB received subcontract grants to Penn State from Proactive Life LLC (formerly Mobile Sleep Technologies) doing business as SleepScape (NSF/STTR #1622766, NIH/NIA SBIR R43-AG056250, R44-AG056250), received honoraria/travel support for lectures from Boston University, Boston College, Tufts School of Dental Medicine, New York University, University of Miami, and Allstate, consulting fees from SleepNumber, and receives an honorarium for his role as the Editor-in-Chief of Sleep Health (sleephealthjournal.org). MLW received consulting fees from HealthRhythm, Sleep Number Corporation, and Noctem, unrelated to this work. Outside of the current work, MAG received grants from Kemin Foods, Jazz Pharmaceuticals, and CeraZ; he has received consulting fees from Idorsia Pharmaceuticals, Fitbit, Smartypants Vitamins, Natrol, National Sleep Foundation, American Sleep Medicine Foundation; he has received publishing fees from Elsevier; and he has received speaking honoraria and/or travel support from New York University, University of Maryland, Stavis & Cohen, and Jazz Pharmaceuticals. SR and YZ are supported by NIH contract, 75N92019C00011; National Sleep Research Resource. The remaining authors have no conflicts to disclose., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: To examine the relationships between self-reported sleep characteristics and risk of incident vertebral fracture and hip fracture in women., Design: Longitudinal cohort study., Setting: Nurses' Health Studies (NHS: 2002-2014, NHSII: 2001-2015)., Participants: Total 122,254 female registered nurses (46,129 NHS, 76,125 NHSII) without prior history of fracture., Exposure: Sleep was characterized by 4 sleep-related domains-sleep duration, sleep difficulty, snoring, and excessive daytime sleepiness-assessed by self-reported questionnaires., Outcomes: Self-reports of vertebral fracture were confirmed by medical record review and hip fracture was assessed by biennial questionnaires., Results: Over 12-14 years of follow-up, 569 incident vertebral fracture cases (408 in NHS, 161 in NHSII) and 1,881 hip fracture cases (1,490 in NHS, 391 in NHSII) were documented. In the pooled analysis, the multivariable-adjusted HR (95% CI) for vertebral fracture was 1.20 (0.86, 1.66) for sleep duration ≤5 hours vs. 7 hours and 0.82 (0.60, 1.12) for ≥9 vs. 7 hours; 1.63 (0.93, 2.87) for sleep difficulties all-the-time vs. none/little-of-the-time (p-trend = 0.005); 1.47 (1.05, 2.05) for snoring every night/week vs. never/occasionally (p-trend = 0.03), and 2.20 (1.49, 3.25) for excessive daytime sleepiness daily vs. never (p-trend < 0.001). In contrast, associations were not observed with hip fracture risk., Conclusion: Poorer sleep characteristics were associated with risk of vertebral fracture. Our study highlights the importance of multiple dimensions of sleep in the development of vertebral fractures. Further research is warranted to understand the role of sleep in bone health that may differ by fracture site, as well as sleep interventions that may reduce the risk of fracture., (Published by Elsevier Inc.)

Purpose: To illustrate 2 frameshifts of multidimensional sleep health: i) use of composite sleep metrics; and ii) the correlations among sleep dimensions., Participants: 735 adults of diverse backgrounds aged <65 years who participated in the Multi-Ethnic Study of Atherosclerosis., Measures: In-home polysomnography, 7-day wrist actigraphy, and validated questionnaires., Methods: The Buysse Ru SATED model-sleep regularity, satisfaction, alertness, timing, efficiency, duration-was operationalized, then extended by including additional measures of sleep architecture and sleep apnea from polysomnography and difficulties initiating sleep from questionnaire and sleep onset latency and duration [ir]regularity from actigraphy. We dichotomized sleep variables, operationalizing optimal and nonoptimal ranges as 1 and 0, respectively, summed into a sleep health score, and computed global sleep health scores via principal components analysis., Findings: Participants showed low prevalence of sleep regularity in timing (<30 minutes standard deviation [SD]; 21.4% favorable) and duration (<60 minutes SD; 36.9%). Although 62.7% of participants demonstrated favorable sleep duration by actigraphy, few met criteria for favorable levels of % N3 (11.4%) or %R (34.1%). The average Sleep Health Score was 5.6 of 13 (higher is better). Sleep variables were variably intercorrelated (r = 0 to r = -0.72). The first principal component for each operationalization of sleep health was interpretable as a "health" score; all summary scores captured variable but systematic shifts towards more favorable sleep in each sleep variable., Conclusions: Multidimensional sleep health can be measured by complementary composite scores as well as consideration of multiple individual dimensions., (Copyright © 2021 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.)

Objective: In this study, we examine associations between objectively measured weekend night vs. school night sleep patterns, weight status, and weight-related behaviors among adolescents., Design: Cross-sectional study., Setting: Five Minnesota high schools that started early (7:30 or 7:45 AM) in Spring 2016., Participants: Ninth grade students, ages 14.5-16 years (n = 284)., Measurements: Students completed surveys, had body measurements taken, and wore sleep (wrist) actigraphs for 1 week (n = 284). We examined weekend night-school night differences in sleep duration and sleep timing. We then assessed whether these factors were related to weight status and weight-related behaviors (eating behaviors, food consumption, physical activity, beverage consumption) using generalized linear mixed models., Results: On average, students slept 1.5 hours (95% confidence interval 1.3-1.7) more and had a sleep midpoint 1.9 hours (1.8-2.1) later on weekend nights compared to school nights. Female students had larger increases in sleep duration on weekend nights than males but similar timing differences. Sleep duration differences were uncorrelated with sleep timing differences (r = 0.01). Neither duration nor timing differences were associated with overweight, obesity, or any of the eating behaviors we examined. However, sleeping longer on weekend nights than on school nights was associated with lower probability of being active 6-7 days per week (p = .02)., Conclusions: Adolescents have substantial sleep duration and sleep timing differences on weekend nights vs. school nights. While these differences may not be associated with weight status or weight-related behaviors, they reflect the reality that most adolescents have schedules that restrict their sleep., (Copyright © 2021 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.)

Introduction: There is a paucity of data exploring the extent that preclinical cognitive changes are predictive of subsequent sleep outcomes., Methods: Logistic regression models were used to evaluate data from a cohort of 196 African American adults who had measures of cognitive function assessed at 2 time points during a 20-year period across the mid- to late-life transition. Cognitive testing included the Delayed Word Recall, the Digit Symbol Substitution, and the Word Fluency tests, which were summarized as a composite cognitive z-score. Sleep apnea was measured by in-home sleep apnea testing and sleep duration and quality were derived from 7-day wrist actigraphy at the end of the study period., Results: A one standard deviation (SD) lower composite cognitive z-score at baseline was significantly associated with greater odds of low sleep efficiency (<85%) (odds ratio [OR] = 1.85, 95% confidence interval [CI] = 1.13, 3.04) and greater odds of increased wakefulness after sleep onset time (WASO; >60 minutes) (OR = 1.65, 95% CI = 1.05, 2.60) in adjusted models. A one SD faster rate of cognitive decline over the study period was significantly associated with greater odds of low sleep efficiency (OR = 1.68, 95% CI = 1.04, 2.73), greater odds of sleep fragmentation (>35%); (OR = 1.73, 95% CI = 1.05, 2.85), and greater odds of increased WASO (OR = 1.85, 95% CI = 1.15, 2.95) in adjusted models. Neither baseline cognitive z-score nor rate of cognitive decline was associated with sleep apnea or the total average sleep duration., Conclusion: Cognition at baseline and change over time predicts sleep quality and may reflect common neural mechanisms and vulnerabilities., (Copyright © 2021 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.)

Objective: We investigated associations of sleep duration and social jetlag with cardiometabolic outcomes., Participants: Boys and girls aged 8-16 years from the Hispanic Community Health Study/Study of Latino Youth., Measurements: Participants (n = 1,208) completed a clinical examination where anthropometric characteristics, health behaviors, and health history were measured. Sleep duration was calculated as the weighted average of self-reported weekday and weekend bedtimes and wake times and categorized into age-specific cutoffs for short vs. normal sleep. "Social jetlag" was defined as the absolute difference in the midpoint of the sleep period between weekdays and weekends, measured continuously and dichotomized (≥2 hours), with higher values indicating more displacement of sleep timing across the week. Regression models tested the associations between sleep measures (separately) and cardiometabolic outcomes (e.g., healthy eating index [0-100], physical activity-minutes per week, obesity, diabetes, hypertension) after adjustment for covariates., Results: The average sleep duration was 9.5 hours (95% confidence interval: 9.3, 9.6) and the mean social jetlag was 2.5 (2.4, 2.7) hours. Participants with social jetlag reported more physical activity (β = 34.8 [13.14], P < .01), had a higher healthy eating index (β = 1.77 [0.87], P < .05] and lower odds of being overweight [OR = 0.66, (95% confidence interval 0.44, 0.99)]. Short sleep duration was associated with less physical activity but did not relate to other cardiometabolic outcomes., Conclusions: Social jetlag was associated with healthier behaviors and a lower odds of being overweight. Given these mixed findings, future research should further evaluate how to best characterize sleep timing differences in youth to identify health consequences., (Copyright © 2020 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.)

Objective: To examine the association of maternal lifetime experiences of racial discrimination with infant sleep duration over the first 2 years of life., Design: Prebirth cohort study., Setting: Massachusetts, USA (baseline: 1999-2002)., Participants: 552 mother-infant dyads in Project Viva, for whom the mother self-identified as being a woman of color., Measurements: During pregnancy, mothers completed the Experiences of Discrimination survey that measured lifetime experiences of racial discrimination in eight domains. The main outcome was a weighted average of their infants' 24-hour sleep duration from 6 months to 2 years., Results: 30% reported 0 domains of racial discrimination, 35% 1-2 domains, and 34% ≥3 domains. Any racial discrimination (≥1 vs. 0 domains) was higher among black (80%) versus Hispanic (58%) or Asian (53%) mothers and the United States versus foreign-born mothers (79% vs. 58%) and was associated with higher mean prepregnancy BMI (26.8 vs. 24.5 kg/m 2 ). Children whose mothers reported ≥3 domains versus 0 domains had shorter sleep duration from 6 months to 2 years in unadjusted analysis (β -18.6 min/d; 95% CI -37.3, 0.0), which was attenuated after adjusting for maternal race/ethnicity and nativity (-13.6 min/d; -33.7, 6.5). We found stronger associations of racial discrimination with offspring sleep at 6 months (-49.3 min/d; -85.3, -13.2) than for sleep at 1 year (-13.5 min/d; -47.2, 20.3) or 2 years (4.2 min/d; -21.5, 29.9)., Conclusions: Maternal lifetime experiences of racial discrimination was associated with shorter offspring sleep duration at 6 months, but not with infant's sleep at 1 and 2 years of age., (Copyright © 2020 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.)

Objectives: The objective of this study is to test the hypothesis that short sleep duration is associated with fewer minutes of transportation, work, and leisure physical activity (PA)., Design: This is a cross-sectional study conducted from 2008 to 2011., Setting: The study setting included four sites across the U.S. (Bronx, NY; Chicago, IL; Miami, FL; San Diego, CA)., Participants: A total of 14,653 Hispanic/Latino adults aged 18-74 years were enrolled as participants for the study., Measurements: Respondents reported sleep duration and transportation (including walking and cycling), work (including volunteering, paid work, and household chores), and leisure (including sports) PA domains and sociodemographic characteristics, other sleep characteristics, cardiometabolic health, health behaviors, and depressive symptoms., Results: In analyses weighted to reflect the Hispanic/Latino population of the four cities sampled, 61% had sleep duration 7-9 hours, 19% each had sleep duration < 7 hours and > 9 hours. Those sleeping < 7 hours spent 106 minutes/day in work-related PA, compared with those who spent fewer than 40 minutes/day in transportation-related or leisure-related PA. Sleep duration < 7 hours was associated with 26 minutes more in work-related PA (95% confidence interval [CI]: 16.7, 36.0), compared with sleep duration of 7-9 hours, adjusting for age and sex. Results were similar in employed respondents only, adjusting for occupation class and shift work frequency. Sleep duration was not associated with transportation-related or leisure-related PA., Conclusions: Short sleep duration is associated with more work-related PA, both in the overall sample and among those employed. Individuals with higher work-related PA may face multiple demands and stressors that negatively influence sleep duration., (Copyright © 2019 National Sleep Foundation. All rights reserved.)

Background: Despite the high prevalence of inadequate sleep in racially/ethnically diverse, low-income adults, there is scant research targeting sleep health interventions among underserved populations. Sleep hygiene (SH) recommendations may help promote sleep health for the general population; however, they likely require tailoring to optimize uptake and effectiveness in the "real world" given socio-contextual factors. As an initial step to developing contextually appropriate and effective community-based SH interventions, we conducted qualitative research to understand SH behaviors, beliefs, and barriers in a low-income, ethnically diverse sample of adults., Methods: We recruited 24 racially/ethnically diverse adults from an affordable housing community who self-reported sleeping ≤6 hours on average. Participants were invited to either an individual interview (n = 5) or a focus group (n = 3). A deductive, thematic-analysis approach was employed. Data collection and interpretation were informed by the Socio-Contextual Model of Behavior Change., Results: There was evidence of high acceptability of SH and interest in improving sleep health. Barriers to implementing SH were multifaceted, including individual (knowledge, motivation, habits, medical issues, stress, trauma), interpersonal (caregiving), organizational (job strain), and environmental (noise) factors., Conclusions: Future strategies for adapting behavioral SH interventions should target knowledge, skill development, and behavioral change domains, such as motivation, social support, and self-efficacy. In addition, adapting SH beyond the clinical context for a high-need community population requires attention to multilevel sociocontextual factors that contribute to sleep health, particularly chronic stress, prior trauma, and adverse sleeping environments. Development of novel trauma-informed SH interventions may promote effective and safe implementation., (Copyright © 2019 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.)

Objective: The objective of this study was to examine the association between perceived stress and sleep health among a sample of sexual minority men (SMM)., Design: Cross-sectional survey., Setting: Paris, France., Participants: Gay, bisexual and other SMM users ≥18 years on a geosocial networking application in Paris, France (N = 580)., Measurements: Participants were directed to a web-based survey measuring stress, sleep health, and socio-demographics. Multivariate log-binomial regression models were used to estimate the adjusted risk ratios (aRRs) and 95% confidence intervals (CIs) to examine how stress may affect different dimensions of sleep health: 1) poor sleep quality, 2) short sleep duration, 3) problems falling asleep, and 4) problems staying awake in the daytime., Results: Most participants (69.9%) reported at least sometimes feeling stressed (compared to never or rarely). Additionally, results demonstrate that higher perceived stress was associated with poorer sleep health; compared with those who reported feeling stress never or rarely, those who felt stress sometimes, often, or always were more likely to experience poor sleep quality (aRR = 6.67; 95% CI = 3.61-12.3), short sleep duration (aRR = 1.67; 95% CI = 1.17-2.38), problems falling asleep (aRR = 3.20; 95% CI = 2.26-4.52), and problems staying awake during the daytime (aRR = 3.52; 95% CI = 1.64-7.53)., Conclusion: Elevated perceived stress can negatively influence sleep health among SMM in Paris, France., (Copyright © 2018 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.)

Objective: Insufficient sleep is common among caregivers and is associated with worse health outcomes; however, the contributors to poor sleep among caregivers are unknown. We investigated the cross-sectional association between socioeconomic status (SES), psychosocial stressors, and sleep among caregivers., Methods: Caregivers (n=98) of teenagers with asthma self-reported sleep duration (hours), sleep quality (very good to very bad), education (

Increasingly, behavioral and epidemiological research uses activity-based measurements (accelerometry) to provide objective estimates of physical activity, sedentary behavior, and sleep in a variety of study designs. As interest in concurrently assessing these domains grows, there are key methodological considerations that influence the choice of monitoring instrument, analysis algorithm, and protocol for measuring these behaviors. The purpose of this review is to summarize evidence-guided information for 7 areas that are of importance in the design and interpretation of studies using actigraphy: (1) choice of cut-points; (2) impact of epoch length; (3) accelerometer placement; (4) duration of monitoring; (5) approaches for distinguishing sleep, nonwear times, and sedentary behavior; (6) role for a sleep and activity diary; and (7) epidemiological applications. Recommendations for future research are outlined and are intended to enhance the appropriate use of accelerometry for assessing physical activity, sedentary behavior, and sleep behaviors in research studies., (Copyright © 2015 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.)