An Aggregate Measure of Sleep Health Is Associated With Prevalent and Incident Clinically Significant Depression Symptoms Among Community-Dwelling Older Women

Furihata, Ryuji; Hall, Martica; Stone, Katie L.; Ancoli‐Israel, Sonia; Smagula, Stephen F.; Cauley, Jane A.; Kaneita, Yoshitaka; Uchiyama, Makoto; Buysse, Daniel J.

doi:10.1093/sleep/zsw075

Cited by 92 publications

(78 citation statements)

References 46 publications

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“…The cutoff values for sleep duration were notably shorter than previous reports. Our results indicated that 5 h 20 min-7 h 6 min represented "good" sleep duration whereas other studies have used values of 7-8 h [7], 6-8 h [3][4][5], and 7-9 h [6]. However, actigraphy was used to measure sleep in the present study, while sleep durations were self-reported in prior studies of sleep health.…”

Section: Discussionmentioning

confidence: 53%

“…Other studies have used sleep latency and wake after sleep onset to operationalize the sleep efficiency dimension of sleep health. In these studies, less than 30 min of sleep latency [6] or combined sleep latency and wake after sleep onset [3][4][5] is considered to be consistent with "good" sleep continuity.…”

Section: Discussionmentioning

confidence: 99%

“…These six sleep dimensions have been shown to load onto a single sleep health factor and demonstrate good construct validity [4]. Sleep health scores outperformed a single measure of sleep duration in predicting poor self-reported health in ROC analysis and are associated with physical and mental health risk in a linear, graded fashion [5,6]. Sleep health scores also appear to be sensitive to factors, such as sex, race, education, and stress [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Despite its increasing use in research, the optimal cutoff values used to differentiate "good" and "poor" across each sleep health dimension remain to be determined. For example, "good" sleep duration has been defined as 6-8 h [3], 7-8 h [7], and 7-9 h [6] across different studies, whereas "good" sleep efficiency has been defined as a sleep latency of <30 min [6], <30 min of wake time at night (including latency and wake after sleep onset) [3][4][5], or a sleep efficiency of ≥85% [7] in independent reports. Whereas all published cutoff values are well-wrought and justified using a mixture of published findings and data from meta-analyses and large representative cohort studies, no study, to our knowledge, has attempted to empirically derive cutoff values for each sleep health dimension.…”

Section: Introductionmentioning

confidence: 99%

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Empirical derivation of cutoff values for the sleep health metric and its relationship to cardiometabolic morbidity: results from the Midlife in the United States (MIDUS) study

Brindle

Buysse

et al. 2019

Sleep

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Study Objectives: Emerging evidence supports a multidimensional perspective of sleep in the context of health. The sleep health model, and composite sleep health score, are increasingly used in research. However, specific cutoff values that differentiate "good" from "poor" sleep, have not been empirically derived and its relationship to cardiometabolic health is less-well understood. We empirically derived cutoff values for sleep health dimensions and examined the relationship between sleep health and cardiometabolic morbidity.Methods: Participants from two independent Biomarker Studies in the MIDUS II (N = 432, 39.8% male, age = 56.92 ± 11.45) and MIDUS Refresher (N = 268, 43.7% male, age = 51.68 ± 12.70) cohorts completed a 1-week study where sleep was assessed with daily diaries and wrist actigraphy. Self-reported physician diagnoses, medication use, and blood values were used to calculate total cardiometabolic morbidity. Receiver operating characteristic (ROC) curves were generated in the MIDUS II cohort for each sleep health dimension to determine cutoff values. Using derived cutoff values, logistic regression was used to examine the relationship between sleep health scores and cardiometabolic morbidity in the MIDUS Refresher cohort, controlling for traditional risk factors.Results: Empirically derived sleep health cutoff values aligned reasonably well to cutoff values previously published in the sleep health literature and remained robust across physical and mental health outcomes. Better sleep health was significantly associated with a lower odds of cardiometabolic morbidity (OR [95% CI] = 0.901 [0.814-0.997], p = .044). Conclusions:These results contribute to the ongoing development of the sleep health model and add to the emerging research supporting a multidimensional perspective of sleep and health. Statement of SignificanceDespite the increasing use of the sleep health model in research, cutoff values that differentiate "good" from "poor" sleep have not been empirically derived and its relationship to cardiometabolic health is less-well understood. We empirically derived cutoff values for each sleep health dimension and examined whether sleep health related to cardiometabolic morbidity. Empirically derived sleep health cutoff values reasonably aligned to cutoff values previously published in the sleep health literature and were robust across mental and physical health outcomes. Poor sleep health was significantly associated with a greater prevalence of cardiometabolic morbidity. These results contribute to the development of the sleep health model and add supportive evidence for a multidimensional perspective of sleep.

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Section: Discussionmentioning

confidence: 53%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Empirical derivation of cutoff values for the sleep health metric and its relationship to cardiometabolic morbidity: results from the Midlife in the United States (MIDUS) study

Brindle

Buysse

et al. 2019

Sleep

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“…Accumulating evidence suggests that, beyond sleep duration alone, each of these sleep health dimensions are individually associated with health risks (9). More importantly, aggregate or composite measures of sleep health are associated in a graded fashion with health risks (10).…”

mentioning

confidence: 99%

Evidence-Based Guidelines for Fatigue Risk Management in Emergency Medical Services: A Step in the Right Direction Toward Better Sleep Health

Buysse¹

2018

Prehospital Emergency Care

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Sleep Disorders Among Individuals With Rheumatoid Arthritis

Katz

Pedro

Michaud

2023

Arthritis Care & Research

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Objective Self‐reported sleep problems are common in rheumatoid arthritis (RA), with potential negative health implications, yet relatively little research has focused on sleep in RA. We examined the prevalence of obstructive sleep apnea (OSA) risk, restless legs syndrome (RLS) symptoms, and short sleep (SS) in a large RA cohort (n = 4,200) and factors associated with each. Methods Data are from FORWARD, The National Databank for Rheumatic Diseases. Validated screening measures assessed OSA risk and RLS symptoms. SS was based on self‐reported average sleep time (<6 hours). The Medical Outcomes Study Sleep Problems Index I measured self‐reported sleep quality. Multivariable logistic regression models identified independent predictors of sleep disorders and sleep quality and the independent association of OSA risk, RLS symptoms, and SS with self‐reported poor sleep quality. Results Twenty‐one percent (n = 899) had OSA diagnosis or risk, 30% (n = 1,272) had RLS symptoms or diagnosis, and 43% (n = 1,737) reported SS, and RA disease activity was associated with each sleep disorder. Abatacept use was associated with lower odds of RLS and SS. Use of conventional disease‐modifying antirheumatic drugs or abatacept was less frequent in the SS group. No medications were associated with OSA in multivariable analyses. Both RLS and SS was associated with worse perceived sleep quality. Discussion Almost two‐thirds met criteria for at least one sleep disorder. RA disease activity and pain were significantly associated with each sleep condition. Additional research is needed to identify the causal pathway between sleep disorders and RA disease activity and pain and the long‐term consequences of sleep disorders in RA.

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An Aggregate Measure of Sleep Health Is Associated With Prevalent and Incident Clinically Significant Depression Symptoms Among Community-Dwelling Older Women

Abstract: An aggregate, multidimensional measure of sleep health was associated with both prevalent and incident clinically-significant depression symptoms in a gradient fashion. Future studies are warranted to extend these findings in different populations and with different health outcomes.

Cited by 92 publications

References 46 publications

Empirical derivation of cutoff values for the sleep health metric and its relationship to cardiometabolic morbidity: results from the Midlife in the United States (MIDUS) study

Empirical derivation of cutoff values for the sleep health metric and its relationship to cardiometabolic morbidity: results from the Midlife in the United States (MIDUS) study

Evidence-Based Guidelines for Fatigue Risk Management in Emergency Medical Services: A Step in the Right Direction Toward Better Sleep Health

Sleep Disorders Among Individuals With Rheumatoid Arthritis

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