Wearable sleep monitors are of high interest to consumers and researchers because of their ability to provide estimation of sleep patterns in free-living conditions in a cost-efficient way.
We ...conducted a systematic review of publications reporting on the performance of wristband Fitbit models in assessing sleep parameters and stages.
In adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we comprehensively searched the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane, Embase, MEDLINE, PubMed, PsycINFO, and Web of Science databases using the keyword Fitbit to identify relevant publications meeting predefined inclusion and exclusion criteria.
The search yielded 3085 candidate articles. After eliminating duplicates and in compliance with inclusion and exclusion criteria, 22 articles qualified for systematic review, with 8 providing quantitative data for meta-analysis. In reference to polysomnography (PSG), nonsleep-staging Fitbit models tended to overestimate total sleep time (TST; range from approximately 7 to 67 mins; effect size=-0.51, P<.001; heterogenicity: I
=8.8%, P=.36) and sleep efficiency (SE; range from approximately 2% to 15%; effect size=-0.74, P<.001; heterogenicity: I
=24.0%, P=.25), and underestimate wake after sleep onset (WASO; range from approximately 6 to 44 mins; effect size=0.60, P<.001; heterogenicity: I
=0%, P=.92) and there was no significant difference in sleep onset latency (SOL; P=.37; heterogenicity: I
=0%, P=.92). In reference to PSG, nonsleep-staging Fitbit models correctly identified sleep epochs with accuracy values between 0.81 and 0.91, sensitivity values between 0.87 and 0.99, and specificity values between 0.10 and 0.52. Recent-generation Fitbit models that collectively utilize heart rate variability and body movement to assess sleep stages performed better than early-generation nonsleep-staging ones that utilize only body movement. Sleep-staging Fitbit models, in comparison to PSG, showed no significant difference in measured values of WASO (P=.25; heterogenicity: I
=0%, P=.92), TST (P=.29; heterogenicity: I
=0%, P=.98), and SE (P=.19) but they underestimated SOL (P=.03; heterogenicity: I
=0%, P=.66). Sleep-staging Fitbit models showed higher sensitivity (0.95-0.96) and specificity (0.58-0.69) values in detecting sleep epochs than nonsleep-staging models and those reported in the literature for regular wrist actigraphy.
Sleep-staging Fitbit models showed promising performance, especially in differentiating wake from sleep. However, although these models are a convenient and economical means for consumers to obtain gross estimates of sleep parameters and time spent in sleep stages, they are of limited specificity and are not a substitute for PSG.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The emerging novel coronavirus disease 2019 (COVID-19) has become one of the leading cause of deaths worldwide in 2020. The present systematic review and meta-analysis estimated the magnitude of ...sleep problems during the COVID-19 pandemic and its relationship with psychological distress.
Five academic databases (Scopus, PubMed Central, ProQuest, ISI Web of Knowledge, and Embase) were searched. Observational studies including case-control studies and cross-sectional studies were included if relevant data relationships were reported (i.e., sleep assessed utilizing the Pittsburgh Sleep Quality Index or Insomnia Severity Index). All the studies were English, peer-reviewed papers published between December 2019 and February 2021. PROSPERO registration number: CRD42020181644.
168 cross-sectional, four case-control, and five longitudinal design papers comprising 345,270 participants from 39 countries were identified. The corrected pooled estimated prevalence of sleep problems were 31% among healthcare professionals, 18% among the general population, and 57% among COVID-19 patients (all p-values < 0.05). Sleep problems were associated with depression among healthcare professionals, the general population, and COVID-19 patients, with Fisher's Z scores of -0.28, -0.30, and -0.36, respectively. Sleep problems were positively (and moderately) associated with anxiety among healthcare professionals, the general population, and COVID-19 patients, with Fisher's z scores of 0.55, 0.48, and 0.49, respectively.
Sleep problems appear to have been common during the ongoing COVID-19 pandemic. Moreover, sleep problems were found to be associated with higher levels of psychological distress. With the use of effective programs treating sleep problems, psychological distress may be reduced. Vice versa, the use of effective programs treating psychological distress, sleep problems may be reduced.
The present study received no funding.
Most existing automated sleep staging methods rely on multimodal data, and scoring a specific epoch requires not only the current epoch but also a sequence of consecutive epochs that precede and ...follow the epoch.
We proposed and tested a convolutional neural network called SleepInceptionNet, which allows sleep classification of a single epoch using a single-channel electroencephalogram (EEG).
SleepInceptionNet is based on our systematic evaluation of the effects of different EEG preprocessing methods, EEG channels, and convolutional neural networks on automatic sleep staging performance. The evaluation was performed using polysomnography data of 883 participants (937,975 thirty-second epochs). Raw data of individual EEG channels (ie, frontal, central, and occipital) and 3 specific transformations of the data, including power spectral density, continuous wavelet transform, and short-time Fourier transform, were used separately as the inputs of the convolutional neural network models. To classify sleep stages, 7 sequential deep neural networks were tested for the 1D data (ie, raw EEG and power spectral density), and 16 image classifier convolutional neural networks were tested for the 2D data (ie, continuous wavelet transform and short-time Fourier transform time-frequency images).
The best model, SleepInceptionNet, which uses time-frequency images developed by the continuous wavelet transform method from central single-channel EEG data as input to the InceptionV3 image classifier algorithm, achieved a Cohen κ agreement of 0.705 (SD 0.077) in reference to the gold standard polysomnography.
SleepInceptionNet may allow real-time automated sleep staging in free-living conditions using a single-channel EEG, which may be useful for on-demand intervention or treatment during specific sleep stages.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Elevated asleep heart rate (HR) is a risk factor for cardiovascular disease and other-cause morbidity and mortality. We assessed the accuracy of Fitbit Inc. PurePulse® photoplethysmography with ...reference to three-lead electrocardiography (ECG) in determining HR during sleep. HR of 35 (17 female) healthy adults 25.1 ± 10.6 years of age (mean ± SD) was continuously recorded throughout a single night of sleep. There was no significant difference in asleep HR mean (0.09 beats per minute bpm,
= 0.426) between Fitbit photoplethysmography and ECG; plus, there was excellent intraclass correlation (0.998) and narrow Bland-Altman agreement range (2.67 bpm). The regression analysis of Bland-Altman plot of mean asleep HR indicates Fitbit tends to slightly overestimate reference values in the lower range of HR (HR < 50 bpm) by 0.51 bpm and slightly underestimate reference values in the higher range of HR (HR > 80 bpm) by 0.63 bpm. Mixed model analysis of epoch-by-epoch (5-min epochs) asleep HR showed significant "U" shape trend (
< 0.001) in amount of Fitbit error (absolute amount of difference between ECG and Fitbit values regardless of overestimation or underestimation) in regard to HR, i.e. smaller error in the medium range of HR (60-80 bpm) and slightly larger error for lower (<60 bpm) and higher (>80 bpm) ranges of HR. However, effect of age, body mass index, gender, and subjective sleep quality measured by Pittsburgh sleep quality index (good/poor sleepers) on error in estimating HR by the Fitbit method was not significant. It is concluded that Fitbit photoplethysmography suitably tracks HR during sleep in healthy young adults.
Performance of wrist actigraphy in assessing sleep not only depends on the sensor technology of the actigraph hardware but also on the attributes of the interpretative algorithm (IA). The objective ...of our research was to improve assessment of sleep quality, relative to existing IAs, through development of a novel IA using deep learning methods, utilizing as input activity count and heart rate variability (HRV) metrics of different window length (number of epochs of data).
Simultaneously recorded polysomnography (PSG) and wrist actigraphy data of 222 participants were utilized. Classic deep learning models were applied to: (a) activity count alone (without HRV), (b) activity count + HRV (30-s window), (c) activity count + HRV (3-min window), and (d) activity count + HRV (5-min window) to ascertain the best set of inputs. A novel deep learning model (Haghayegh Algorithm, HA), founded on best set of inputs, was developed, and its sleep scoring performance was then compared with the most popular University of California San Diego (UCSD) and Actiwatch proprietary IAs.
Activity count combined with HRV metrics calculated per 5-min window produced highest agreement with PSG. HA showed 84.5% accuracy (5.3-6.2% higher than comparator IAs), 89.5% sensitivity (6.2% higher than UCSD IA and 6% lower than Actiwatch proprietary IA), 70.0% specificity (8.2-34.3% higher than comparator IAs), and 58.7% Kappa agreement (16-23% higher than comparator IAs) in detecting sleep epochs. HA did not differ significantly from PSG in deriving sleep parameters-sleep efficiency, total sleep time, sleep onset latency, and wake after sleep onset; moreover, bias and mean absolute error of the HA model in estimating them was less than the comparator IAs. HA showed, respectively, 40.9% and 54.0% Kappa agreement with PSG in detecting rapid and non-rapid eye movement (REM and NREM) epochs.
The HA model simultaneously incorporating activity count and HRV metrics calculated per 5-min window demonstrates significantly better sleep scoring performance than existing popular IAs.
We examined associations between dog ownership, morning dog walking and its timing and duration, and depression risk in female nurses, exploring effect modification by chronotype. We hypothesized ...that dog ownership and morning walking with the dog are associated with lower odds of depression, and that the latter is particularly beneficial for evening chronotypes by helping them to synchronize their biological clock with the solar system.
26,169 depression-free US women aged 53-72 from the Nurses' Health Study 2 (NHS2) were prospectively followed from 2017-2019. We used age- and multivariable-adjusted logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (95%CIs) for depression according to dog ownership, and morning dog walking, duration, and timing.
Overall, there was no association between owning a dog (ORvs_no_pets = 1.12, 95%CI = 0.91-1.37), morning dog walking (ORvs_not = 0.87, 95%CI = 0.64-1.18), or the duration (OR>30min vs. ≤15mins = 0.68, 95%CI = 0.35-1.29) or timing of morning dog walks (ORafter9am vs. before7am = 1.06, 95%CI = 0.54-2.05) and depression. Chronotype of dog owners appeared to modify these associations. Compared to women of the same chronotype but without pets, dog owners with evening chronotypes had a significantly increased odds of depression (OR = 1.60, 95%CI = 1.12-2.29), whereas morning chronotypes did not (OR = 0.94, 95%CI = 0.71-1.23). Further, our data suggested that evening chronotypes benefited more from walking their dog themselves in the morning (OR = 0.75, 95%CI = 0.46-1.23, Pintx = 0.064;) than morning chronotypes.
Overall, dog ownership was not associated with depression risk though it was increased among evening chronotypes. Walking their dog in the morning might help evening chronotypes to lower their odds of depression, though more data are needed to confirm this finding.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Night shift work has been associated with breast, prostate, and colorectal cancer, but evidence on other types of cancer is limited. We prospectively evaluated the association of rotating night shift ...work, sleep duration, and sleep difficulty with thyroid cancer risk in the Nurses' Health Study 2 (NHS2). We assessed rotating night shift work duration (years) at baseline and throughout follow-up (1989-2015) and sleep characteristics in 2001. Cox proportional hazard models, adjusted for potential confounders, were used to calculate hazard ratios (HR) and 95% confidence intervals (CI) for (a) shift work duration, (b) sleep duration, and (c) difficulty falling or staying asleep. We stratified the analyses of night shift work by sleep duration and sleep difficulty. Over 26 years of follow-up, 588 incident cases were identified among 114,534 women in the NHS2 cohort. We observed no association between night shift work and the risk of thyroid cancer. Difficulty falling or staying asleep was suggestively associated with a higher incidence of thyroid cancer when reported sometimes (HR 1.26, 95% CI 0.95, 1.66) and all or most of the time (HR 1.35, 95% CI 1.00, 1.81). Night shift workers (10+ years) with sleep difficulty all or most of the time (HR 1.47; 0.58-3.73) or with >7 h of sleep duration (HR 2.17; 95% CI, 1.21-3.92) had a higher risk of thyroid cancer. We found modest evidence for an increased risk of thyroid cancer in relation to sleep difficulty, which was more pronounced among night shift workers.
The relationship between 24-hour rest-activity rhythms (RARs) and risk for dementia or mild cognitive impairment (MCI) remains an area of growing interest. Previous studies were often limited by ...small sample sizes, short follow-ups, and older participants. More studies are required to fully explore the link between disrupted RARs and dementia or MCI in middle-aged and older adults.
We leveraged the UK Biobank data to examine how RAR disturbances correlate with the risk of developing dementia and MCI in middle-aged and older adults.
We analyzed the data of 91,517 UK Biobank participants aged between 43 and 79 years. Wrist actigraphy recordings were used to derive nonparametric RAR metrics, including the activity level of the most active 10-hour period (M10) and its midpoint, the activity level of the least active 5-hour period (L5) and its midpoint, relative amplitude (RA) of the 24-hour cycle RA=(M10-L5)/(M10+L5), interdaily stability, and intradaily variability, as well as the amplitude and acrophase of 24-hour rhythms (cosinor analysis). We used Cox proportional hazards models to examine the associations between baseline RAR and subsequent incidence of dementia or MCI, adjusting for demographic characteristics, comorbidities, lifestyle factors, shiftwork status, and genetic risk for Alzheimer's disease.
During the follow-up of up to 7.5 years, 555 participants developed MCI or dementia. The dementia or MCI risk increased for those with lower M10 activity (hazard ratio HR 1.28, 95% CI 1.14-1.44, per 1-SD decrease), higher L5 activity (HR 1.15, 95% CI 1.10-1.21, per 1-SD increase), lower RA (HR 1.23, 95% CI 1.16-1.29, per 1-SD decrease), lower amplitude (HR 1.32, 95% CI 1.17-1.49, per 1-SD decrease), and higher intradaily variability (HR 1.14, 95% CI 1.05-1.24, per 1-SD increase) as well as advanced L5 midpoint (HR 0.92, 95% CI 0.85-0.99, per 1-SD advance). These associations were similar in people aged <70 and >70 years, and in non-shift workers, and they were independent of genetic and cardiovascular risk factors. No significant associations were observed for M10 midpoint, interdaily stability, or acrophase.
Based on findings from a large sample of middle-to-older adults with objective RAR assessment and almost 8-years of follow-up, we suggest that suppressed and fragmented daily activity rhythms precede the onset of dementia or MCI and may serve as risk biomarkers for preclinical dementia in middle-aged and older adults.
Abstract Introduction Physiological outputs such as motor activity display complex fluctuations with a delicate pattern balanced between randomness and excessive regularity. Altered fractal patterns ...were observed in patients with Parkinson’s disease (PD). We examined whether perturbed motor activity fluctuation patterns are associated with the risk of developing PD in middle-to-older aged adults. Methods Actigraphy recordings (up to 7 days) were collected from more than 100,000 participants in the UK Biobank between 2013-2015. Participants were followed after actigraphy assessments for up to 7.5 years (median: 5 years). The detrended fluctuation analysis was performed to obtain a scaling exponent, α, that quantifies temporal correlations in activity fluctuations at timescales ~6-90 min: α close to 1 indicates the highest complexity or the balance between randomness and excessive regularity as observed in health young adults; deviations from 1 indicate reduced complexity — more randomness (close to 0.5) or excessive regularity or rigidity (close to 1.5) as occurred with aging or in diseases. We performed Cox proportional hazards models to examine the association of |α-1| with incident PD (ascertained by ICD-10 codes) during the follow-up while adjusting for age, sex, education, ethnicity, obesity, sleep apnea, alcohol intake, smoking status, morbidity burden, circulatory system disorder, and Townsend Deprivation Index (TDI). Results In total, 94,041 participants (56.4% females; age: 62.4±7.83 SD, range 43.5-79.0 years) who had valid actigraphy or had no PD diagnosis at baseline were included. Among them, 290 participants (~0.3%) developed PD after 3.7±1.8 SD years from baseline. Older age (hazard ratio HR per 1 year increase=1.15, 95% CI: 1.12-1.17, p< 0.0001) and being male (HR= 2.42, 95% CI: 1.88-3.13, p< 0.0001) were associated with a higher risk of developing PD. The mean and SD of the squared root transformed |α-1| were 0.2±0.1. With a square root transform on |α-1| (to account for the right skewed distribution) and after controlling for co-variables, larger |α-1| was associated with increased risk of developing PD (for 1-SD increase, HR=1.15, 95% CI: 1.02-1.28, p=0.017; Q4 vs. Q1: HR=1.46, 95% CI: 1.05-2.04, p=0.025). Conclusion Perturbed balance between randomness and regularity in motor activity fluctuations was associated with higher PD risk. Support (if any) 5T32HL007901-25, NIH RF1AG064312, BrightFocus A2020886S.
Abstract
Introduction
Alzheimer’s disease (AD) causes disruptions in brain functions, which can be tracked by proper analysis of electroencephalography (EEG). Current efforts to understand the impact ...of AD on brain activity have leveraged the development of high-density EEG. Alternatively, we propose that AD can be discriminated from healthy controls (HC) by analyzing low-density EEG sleep recordings with nonlinear measures that capture functional interactions beyond pairwise such as the total correlation (TC), dual total correlation (DTC), O-information (O) and S-information (S).
Methods
We analyzed overnight polysomnography data with 4 EEG channels (left central, right central, left mastoid, and right mastoid) of 28 older men (14 with a self-reported history of clinical diagnosis of AD and 14 HC, matched in age and education) in the Osteoporotic Fractures in Men Study (MrOS) obtained from the National Sleep Research Resource (NSRR). Each 30-second epoch was scored as wake (W), N1, N2, N3, or rapid eye movement. Features based on low (2) and high order (3-4) interactions between electrodes (TC, DTC, O, and S) were extracted from raw and band-pass filtered data within each wake/sleep stage. Finally, a feature selection algorithm provided the optimal subset of features that maximizes the discrimination between conditions.
Results
AD was discriminated from HC with an average area under the receiver operating characteristic curve of 0.90±0.05 using just 3 features: O (triplet) measured within W stage in the high γ band (40-100 Hz), DTC (triplet) for raw N1 data, and TC (pairwise) in the δ band (0.5-4 Hz) within N stages.
Conclusion
State-of-the-art analysis of high-order interactions can maximize the discrimination of AD using a low-density sleep EEG set-up. The potential of these features to capture the physiopathology of AD and its impact on brain function should be further confirmed using larger samples.
Support (if any)
National Institutes of Health (RF1AG059867, RF1AG064312, R01AG057234); ANID/FONDECYT Regular (1210195 and 1210176 and 1220995); Alzheimer’s Association (SG-20-725707). MrOS Sleep Study was supported by R01 HL071194, R01HL070848, R01HL070847, R01HL070842, R01HL070841, R01HL070837, R01HL070838, and R01HL070839. The NSRR was supported by R24HL114473, 75N92019R002.
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