Sleep continuity and total sleep time are associated with task-switching and preparation in young and older adults

Background Frailty has detrimental health impacts on older home care clients and is associated with increased hospitalization and long-term care admission. The prevalence of frailty among home care clients is poorly understood and ranges from 4.0% to 59.1%. Although frailty screening tools exist, their inconsistent use in practice calls for more innovative and easier-to-use tools. Owing to increases in the capacity of wearable devices, as well as in technology literacy and adoption in Canadian older adults, wearable devices are emerging as a viable tool to assess frailty in this population. Objective The objective of this study was to prove that using a wearable device for assessing frailty in older home care clients could be possible. Methods From June 2018 to September 2019, we recruited home care clients aged 55 years and older to be monitored over a minimum of 8 days using a wearable device. Detailed sociodemographic information and patient assessments including degree of comorbidity and activities of daily living were collected. Frailty was measured using the Fried Frailty Index. Data collected from the wearable device were used to derive variables including daily step count, total sleep time, deep sleep time, light sleep time, awake time, sleep quality, heart rate, and heart rate standard deviation. Using both wearable and conventional assessment data, multiple logistic regression models were fitted via a sequential stepwise feature selection to predict frailty. Results A total of 37 older home care clients completed the study. The mean age was 82.27 (SD 10.84) years, and 76% (28/37) were female; 13 participants were frail, significantly older (P<.01), utilized more home care service (P=.01), walked less (P=.04), slept longer (P=.01), and had longer deep sleep time (P<.01). Total sleep time (r=0.41, P=.01) and deep sleep time (r=0.53, P<.01) were moderately correlated with frailty. The logistic regression model fitted with deep sleep time, step count, age, and education level yielded the best predictive performance with an area under the receiver operating characteristics curve value of 0.90 (Hosmer-Lemeshow P=.88). Conclusions We proved that a wearable device could be used to assess frailty for older home care clients. Wearable data complemented the existing assessments and enhanced predictive power. Wearable technology can be used to identify vulnerable older adults who may benefit from additional home care services.

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0363 Racial/Ethnic Differences in Actigraphy, Questionnaire, and Polysomnography-Measured Indicators of Sleep Health and Sleep Quality: The Multi-Ethnic Study of Atherosclerosis

SLEEP ◽

10.1093/sleep/zsaa056.360 ◽

2020 ◽

Vol 43 (Supplement_1) ◽

pp. A138-A139

Author(s):

J Chung ◽

M O Goodman ◽

T Huang ◽

M Wallace ◽

S Bertisch ◽

...

Keyword(s):

Total Sleep Time ◽

Sleep Onset ◽

Well Being ◽

Sleep Time ◽

Lower Probability ◽

Sleep Onset Latency ◽

Sleep Health ◽

Ethnic Study ◽

Sleep Continuity ◽

Racial Ethnic

Abstract Introduction Paradigm shifts in sleep research suggest the importance of considering multi-dimensional sleep health, compared to single metrics, to promote physical and mental well-being and to understand racial/ethnic disparities in sleep. Methods We used data from the Multi-Ethnic Study of Atherosclerosis (MESA; n = 1,740) to create a Sleep Health Score (SHS), including questionnaire (quality, sleepiness); 7-day actigraphy (total sleep time, sleep continuity [sleep maintenance efficiency], timing consistency [midpoint variability], fragmentation, wake after sleep onset, sleep onset latency); and in-home polysomnography (%N3 sleep, %REM sleep, AHI). Sleep parameters were dichotomized based on prior literature or by healthiest quartile(s), with positive values denoting healthier sleep (e.g. Epworth scores < 10). All 11 dichotomized parameters were summed to calculate the SHS (mean=4.9, sd=1.58). We used modified Poisson and linear regression for individual sleep outcomes and the SHS, respectively, adjusting for age and sex. Results The sample was older (mean age=68.28, sd=9.08) and 54% female. SHSs were associated with Black race (β=-0.60 [-0.78, -0.42]) and Hispanic ethnicity (β=-0.40 [-0.59, -0.21]), but not Chinese ethnicity (β=-0.16 [-0.41, 0.08]). Compared to Whites (n=644), Blacks (n=485) showed lower adjusted probability of obtaining favorable levels of: sleep continuity, fragmentation, timing consistency, alertness/sleepiness, and sleep depth (%N3 sleep). Chinese respondents (n=202) had lower probability of obtaining favorable levels of: sleep continuity and timing consistency, but higher probability of quality. Hispanics (n=409) had lower probability of obtaining healthy levels of: sleep continuity, timing consistency, and fragmentation. Neither healthy total sleep time (middle quartiles) nor AHI (<30) differed by race/ethnicity. Conclusion Among MESA-Sleep participants, summary SHSs were lowest in Blacks, followed by Hispanics. Multiple dimensions of sleep - particularly related to continuity and timing consistency - were less favorable across race/ethnic minority groups. A summary SHS may help monitor sleep health across populations, while measurement of specific sleep components may help identify modifiable targets. Support Joon Chung is supported by a T-32 NIH training grant.

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Caudate Volume Mediates the Interaction between Total Sleep Time and Executive Function after Acute Exercise in Healthy Older Adults

Brain Plasticity ◽

10.3233/bpl-190087 ◽

2019 ◽

Vol 5 (1) ◽

pp. 69-82 ◽

Cited By ~ 2

Author(s):

Junyeon Won ◽

Alfonso J. Alfini ◽

Lauren R. Weiss ◽

Casandra C. Nyhuis ◽

Adam P. Spira ◽

...

Keyword(s):

Older Adults ◽

Executive Function ◽

Acute Exercise ◽

Total Sleep Time ◽

Sleep Time ◽

Healthy Older Adults

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Home-Based Sleep Sensor Measurements in an Older Australian Population: Before and during a Pandemic

Sensors ◽

10.3390/s21185993 ◽

2021 ◽

Vol 21 (18) ◽

pp. 5993

Author(s):

Mahnoosh Kholghi ◽

Claire M. Ellender ◽

Qing Zhang ◽

Yang Gao ◽

Liesel Higgins ◽

...

Keyword(s):

Older Adults ◽

Time Course ◽

Total Sleep Time ◽

Sleep Onset ◽

Sleep Time ◽

Community Dwelling ◽

Sleep Efficiency ◽

Sleep Onset Latency ◽

Onset Latency ◽

Short Sleep Duration

Older adults are susceptible to poor night-time sleep, characterized by short sleep duration and high sleep disruptions (i.e., more frequent and longer awakenings). This study aimed to longitudinally and objectively assess the changes in sleep patterns of older Australians during the 2020 pandemic lockdown. A non-invasive mattress-based device, known as the EMFIT QS, was used to continuously monitor sleep in 31 older adults with an average age of 84 years old before (November 2019–February 2020) and during (March–May 2020) the COVID-19, a disease caused by a form of coronavirus, lockdown. Total sleep time, sleep onset latency, wake after sleep onset, sleep efficiency, time to bed, and time out of bed were measured across these two periods. Overall, there was no significant change in total sleep time; however, women had a significant increase in total sleep time (36 min), with a more than 30-min earlier bedtime. There was also no increase in wake after sleep onset and sleep onset latency. Sleep efficiency remained stable across the pandemic time course between 84–85%. While this sample size is small, these data provide reassurance that objective sleep measurement did not deteriorate through the pandemic in older community-dwelling Australians.

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Role of sleep continuity and total sleep time in executive function across the adult lifespan.

Psychology and Aging ◽

10.1037/a0037234 ◽

2014 ◽

Vol 29 (3) ◽

pp. 658-665 ◽

Cited By ~ 40

Author(s):

Kristine A. Wilckens ◽

Sarah G. Woo ◽

Afton R. Kirk ◽

Kirk I. Erickson ◽

Mark E. Wheeler

Keyword(s):

Executive Function ◽

Total Sleep Time ◽

Sleep Time ◽

Adult Lifespan ◽

Sleep Continuity

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Cannabis Use is Associated with Greater Total Sleep Time in Middle-Aged and Older Adults with and without HIV: A Preliminary Report Utilizing Digital Health Technologies

Cannabis ◽

10.26828/cannabis.2020.02.005 ◽

2020 ◽

Vol 3 (2) ◽

pp. 180-189

Author(s):

Laura Campbell ◽

Bin Tang ◽

Wei-Ming Watson ◽

Michael Higgins ◽

Mariana Cherner ◽

...

Keyword(s):

Older Adults ◽

Preliminary Report ◽

Total Sleep Time ◽

Digital Health ◽

Sleep Time ◽

Cannabis Use ◽

Middle Aged ◽

Health Technologies

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P129 A substantial percentage of patients with Chronic Insomnia do not appreciably increase Total Sleep Time after Cognitive Behavioural Therapy for Insomnia

SLEEP Advances ◽

10.1093/sleepadvances/zpab014.170 ◽

2021 ◽

Vol 2 (Supplement_1) ◽

pp. A63-A63

Author(s):

H Scott ◽

J Cheung ◽

A Muench ◽

H Ivers ◽

M Grandner ◽

...

Keyword(s):

Total Sleep Time ◽

Sleep Onset ◽

Severity Index ◽

Randomised Clinical Trial ◽

Sleep Time ◽

Early Morning ◽

Behavioural Therapy ◽

Sleep Diary ◽

Wake Time ◽

Sleep Continuity

Abstract Introduction Total sleep time (TST) does not exceed baseline for the majority of patients after CBT-I. However by follow-up, TST increases by almost 1 hour on average. The current study investigated the extent to which this TST improvement is common and assessed for baseline predictors of increased TST after CBT-I. Methods This study is an archival analysis of data from a randomised clinical trial comparing acute CBT-I to acute CBT-I plus maintenance therapy (N = 80). The percent of patients that exceeded baseline TST by ≥30 minutes was assessed at post treatment and 3, 6, 12, and 24 months following treatment. Linear mixed models were conducted to assess the effect of patient demographics (age, sex, ethnicity, marital status), and baseline Sleep Diary-reported sleep continuity and Insomnia Severity Index (ISI) scores on changes in TST. Results 17% of patients achieved an appreciable increase in TST by treatment end, and this proportion only increased to 58% over time. Sleep Diary-reported sleep latency, wake after sleep onset, early morning awakenings, total wake time, TST, and sleep efficiency at baseline were associated with greater increases in TST after CBT-I (interaction ps < .03). Demographics and ISI scores were not significant predictors (interaction ps > .07). Conclusion A substantial proportion of patients do not appreciably increase TST after CBT-I, but patients with more severe sleep continuity disturbances at baseline exhibited the largest improvements. Whether all patients could increase their TST even further after CBT-I is a topic for further investigation.

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0828 Descriptive Model of Sleep Quality in the Older Adults

SLEEP ◽

10.1093/sleep/zsaa056.824 ◽

2020 ◽

Vol 43 (Supplement_1) ◽

pp. A315-A316

Author(s):

P K Morelhao ◽

G L Fernandes ◽

V Dokkedal-Silva ◽

G N Pires ◽

S Tufik ◽

...

Keyword(s):

Older Adults ◽

Sleep Quality ◽

Total Sleep Time ◽

Sleep Time ◽

Sleep Efficiency ◽

Poor Sleep ◽

Descriptive Model ◽

Subjective Sleep ◽

Sleep Parameters ◽

The City

Abstract Introduction Poor sleep quality is a health condition that impacts the quality of life of the older population. In the literature, there are questions about which objective sleep parameters should be considered to describe precisely the definition of sleep quality. There is ongoing debate with this term usually being used in relation to subjective sleep perception. This study aimed to investigate which objective and subjective sleep parameters contribute to a measurement of sleep quality in older adults. Methods A cross-sectional study using a representative sample of adults from the city of São Paulo, Brazil was performed. We used a dataset from the 2015 Epidemiological Study of Sleep from the City of São Paulo (EPISONO), including only individuals aged 60 years or more. We used exploratory factor analysis and structural equation modelling to identify relevant variables to a descriptive model of sleep quality. Results A total of 152 older adults were included. The final model consists of two factors, objective sleep quality which comprises sleep efficiency, total sleep time and sleep latency, and poor sleep perception, constituted by scores in the Pittsburgh Sleep Quality Index and Insomnia Severity Index. Conclusion The results suggested that sleep quality had both an objective (sleep efficiency, total sleep time, latency of sleep onset) and subjective dimensions (subjective questionnaires). These results may be useful in the clinical scenario, serving as leads for a better understanding of the sleep quality in aging patients. Future studies may also benefit from this descriptive model to further researches other associations, such as sleep and pain in this population. Support The study was supported by Associação Fundo de Incentivo à Pesquisa (AFIP) and Coordenação de Aperfeiçoamento de Nível Superior (CAPES). ST and MLA received support from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).

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Prevalence and correlates of total sleep time among the older adults during COVID-19 pandemic in Bangladesh

Sleep Epidemiology ◽

10.1016/j.sleepe.2021.100008 ◽

2021 ◽

Vol 1 ◽

pp. 100008

Author(s):

Sabuj Kanti Mistry ◽

ARM Mehrab Ali ◽

Md. Sabbir Ahmed ◽

Uday Narayan Yadav ◽

Md. Safayet Khan ◽

...

Keyword(s):

Older Adults ◽

Total Sleep Time ◽

Sleep Time

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Relationship Between Sleep and Function in Older Adults With Mild Cognitive Impairment

Innovation in Aging ◽

10.1093/geroni/igaa057.2104 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 619-619

Author(s):

Miranda McPhillips ◽

Junxin Li ◽

Darina Petrovsky ◽

Nancy Hodgson

Keyword(s):

Older Adults ◽

Cognitive Impairment ◽

Sleep Quality ◽

Physical Function ◽

Daytime Sleepiness ◽

Total Sleep Time ◽

Social Activity ◽

Sleep Onset ◽

Sleep Time ◽

And Function

Abstract Our objective was to examine relationships between sleep characteristics and function in community-dwelling older adults with cognitive impairment. Sleep measures included actigraphy (total sleep time, wake after sleep onset, efficiency, awakenings), Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale. Promis Physical Function Short Form and Promis Item Bank Social were used to measure physical function and social activity. We used Spearman’s correlation and multivariate linear regression. In bivariate analyses, physical function was significantly related to daytime sleepiness, wake after sleep onset and awakenings; social activity was significantly related to sleep quality, daytime sleepiness, total sleep time, wake after sleep onset and number of awakenings. Controlling for cognition and age, sleep quality was independently associated with physical function (β= -0.80; p= 0.002). Relationships between sleep and social activity did not remain significant in multivariate analyses. Preliminary results suggest subjective sleep quality is most related to physical function.

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