Sleep monitoring with the Apple Watch: comparison to a clinically validated actigraph

Background: We investigate the feasibility of using an Apple Watch for sleep monitoring by comparing its performance to the clinically validated Philips Actiwatch Spectrum Pro (the gold standard in this study), under free-living conditions. Methods: We recorded 27 nights of sleep from 14 healthy adults (9 male, 5 female). We extracted activity counts from the Actiwatch and classified 15-second epochs into sleep/wake using the Actiware Software. We extracted triaxial acceleration data (at 50 Hz) from the Apple Watch, calculated Euclidean norm minus one (ENMO) for the same epochs, and classified them using a similar algorithm. We used a range of analyses, including Bland-Altman plots and linear correlation, to visualize and assess the agreement between Actiwatch and Apple Watch. Results: The Apple Watch had high overall accuracy (97%) and sensitivity (99%) in detecting actigraphy-defined sleep, and adequate specificity (79%) in detecting actigraphy defined wakefulness. Over the 27 nights, total sleep time was strongly linearly correlated between the two devices (r=0.85). On average, the Apple Watch over-estimated total sleep time by 6.31 minutes and under-estimated Wake After Sleep Onset by 5.74 minutes. The performance of the Apple Watch compares favorably to the clinically validated Actiwatch in a normal environment. Conclusions: This study suggests that the Apple Watch could be an acceptable alternative to the Philips Actiwatch for sleep monitoring, paving the way for larger-scale sleep studies using Apple’s consumer-grade mobile device and publicly available sleep classification algorithms. Further study is needed to assess longer-term performance in natural conditions, and against polysomnography in clinical settings.

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P062 Sleep parameter validation of a home-based ballistocardiograph sleep tracker

SLEEP Advances ◽

10.1093/sleepadvances/zpab014.108 ◽

2021 ◽

Vol 2 (Supplement_1) ◽

pp. A41-A42

Author(s):

M Kholghi ◽

I Szollosi ◽

M Hollamby ◽

D Bradford ◽

Q Zhang

Keyword(s):

Total Sleep Time ◽

Sleep Onset ◽

Sleep Time ◽

Daily Routine ◽

Sleep Onset Latency ◽

Onset Latency ◽

Sleep Monitoring ◽

Significant Discrepancy ◽

Home Based ◽

Wake Detection

Abstract Introduction Consumer home sleep trackers are gaining popularity for objective sleep monitoring. Amongst them, non-wearable devices have little disruption in daily routine and need little maintenance. However, the validity of their sleep outcomes needs further investigation. In this study, the accuracy of the sleep outcomes of EMFIT Quantified Sleep (QS), an unobtrusive and non-wearable ballistocardiograph sleep tracker, was evaluated by comparing it with polysomnography (PSG). Methods 62 sleep lab patients underwent a single clinical PSG and their sleep measures were simultaneously collected through PSG and EMFIT QS. Total Sleep Time (TST), Wake After Sleep Onset (WASO), Sleep Onset Latency (SOL) and average Heart Rate (HR) were compared using paired t-tests and agreement analysed using Bland-Altman plots. Results EMFIT QS data loss occurred in 47% of participants. In the remaining 33 participants (15 females, with mean age of 53.7±16.5), EMFIT QS overestimated TST by 177.5±119.4 minutes (p<0.001) and underestimated WASO by 44.74±68.81 minutes (p<0.001). It accurately measured average resting HR and was able to distinguish SOL with some accuracy. However, the agreement between EMFIT QS and PSG on sleep-wake detection was very low (kappa=0.13, p<0.001). Discussion A consensus between PSG and EMFIT QS was found in SOL and average HR. There was a significant discrepancy and lack of consensus between the two devices in other sleep outcomes. These findings indicate that while EMFIT QS is not a credible alternative to PSG for sleep monitoring in clinical and research settings, consumers may find some benefit from longitudinal monitoring of SOL and HR.

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0260 Association Between Free-Living Physical Activity and Sleep in Icelandic Adolescents

SLEEP ◽

10.1093/sleep/zsaa056.258 ◽

2020 ◽

Vol 43 (Supplement_1) ◽

pp. A99-A99

Author(s):

V Rognvaldsdottir ◽

E Johannsson ◽

H M Soffia ◽

R S Stefansdottir ◽

S A Arngrimsson ◽

...

Keyword(s):

Physical Activity ◽

Sleep Quality ◽

Total Sleep Time ◽

Linear Regression Analysis ◽

Sleep Onset ◽

Sleep Time ◽

Free Living ◽

Negatively Associated ◽

Significant Difference ◽

Objectively Measured

Abstract Introduction Sleep and physical activity are both important to health, but the demands of our modern schedule often require individuals to choose one over the other. In adolescents, the association between objectively measured sleep and physical activity is not well established in the literature. The aim of current study was to assess associations between free-living and physical activity and sleep among 15-year-old adolescents. Methods Free-living physical activity and sleep were assessed with wrist-worn accelerometers, sleep diary, and questionnaires during a 7-day period including school days and non-school days in 270 (161 girls) adolescents (mean age 15.8±0.3y) in Reykjavik, Iceland. Linear regression analysis was used to explore the associations between objectively measured physical activity and sleep. T-test was used to determine if there is a significant difference in objectively measured sleep between those who reported sports or exercising <6 versus ≥6 h/week. Results Weekly mean physical activity (2040±466 counts/min of wear/day) was negatively associated with total sleep time (6.6±0.64 h/night) (β±SE=-3.5±0.7, p<0.001). However, physical activity was also negatively associated with minutes of wake after sleep onset on non-school days (p=0.047) and standard deviation (i.e. night-to-night variability) of total sleep time over the week (p=0.028). Subjects who reported exercising ≥6 h/week (n=116) had lower night-to-night variability in bedtime (41.2±27.9 min) than those who did not (49.8±37.5 min), p=0.033. Conclusion The negative association between physical activity and sleep duration suggests that in more active individuals’ physical activity may be displacing sleep. However, greater physical activity is also associated with fewer minutes of awakening and a less variable sleep schedule, indicating better sleep quality. These findings suggest that physical activity is important for good sleep quality, but students should more closely consider sleep guidelines when designing an exercise schedule. Future studies should test how change in sleep patterns might influence physical activity. Support Icelandic Centre for Research, National Institute of Diabetes and Digestive and Kidney Diseases.

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The feasibility and reliability of actigraphy to monitor sleep in intensive care patients: an observational study

Critical Care ◽

10.1186/s13054-020-03447-8 ◽

2021 ◽

Vol 25 (1) ◽

Author(s):

L. J. Delaney ◽

E. Litton ◽

K. L. Melehan ◽

H.-C. C. Huang ◽

V. Lopez ◽

...

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Observational Study ◽

Total Sleep Time ◽

Sleep Time ◽

Moderate Level ◽

Sleep Monitoring ◽

Clinical Trial Registration ◽

Wake Time ◽

Intensive Care Patients

Abstract Background Sleep amongst intensive care patients is reduced and highly fragmented which may adversely impact on recovery. The current challenge for Intensive Care clinicians is identifying feasible and accurate assessments of sleep that can be widely implemented. The objective of this study was to investigate the feasibility and reliability of a minimally invasive sleep monitoring technique compared to the gold standard, polysomnography, for sleep monitoring. Methods Prospective observational study employing a within subject design in adult patients admitted to an Intensive Care Unit. Sleep monitoring was undertaken amongst minimally sedated patients via concurrent polysomnography and actigraphy monitoring over a 24-h duration to assess agreement between the two methods; total sleep time and wake time. Results We recruited 80 patients who were mechanically ventilated (24%) and non-ventilated (76%) within the intensive care unit. Sleep was found to be highly fragmented, composed of numerous sleep bouts and characterized by abnormal sleep architecture. Actigraphy was found to have a moderate level of overall agreement in identifying sleep and wake states with polysomnography (69.4%; K = 0.386, p < 0.05) in an epoch by epoch analysis, with a moderate level of sensitivity (65.5%) and specificity (76.1%). Monitoring accuracy via actigraphy was improved amongst non-ventilated patients (specificity 83.7%; sensitivity 56.7%). Actigraphy was found to have a moderate correlation with polysomnography reported total sleep time (r = 0.359, p < 0.05) and wakefulness (r = 0.371, p < 0.05). Bland–Altman plots indicated that sleep was underestimated by actigraphy, with wakeful states overestimated. Conclusions Actigraphy was easy and safe to use, provided moderate level of agreement with polysomnography in distinguishing between sleep and wakeful states, and may be a reasonable alternative to measure sleep in intensive care patients. Clinical Trial Registration number ACTRN12615000945527 (Registered 9/9/2015).

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Study of correlation between perceived sleep disturbances in depressed patients with objective changes in sleep architecture using polysomnography before and after antidepressant therapy

International Journal of Research in Medical Sciences ◽

10.18203/2320-6012.ijrms20202894 ◽

2020 ◽

Vol 8 (7) ◽

pp. 2551

Author(s):

Ganesh Ingole ◽

Harpreet S. Dhillon ◽

Bhupendra Yadav

Keyword(s):

Sleep Disturbances ◽

Total Sleep Time ◽

Antidepressant Treatment ◽

Sleep Onset ◽

Sleep Time ◽

Sleep Efficiency ◽

Sleep Architecture ◽

Wake Time ◽

Depressed Patients ◽

Before And After

Background: A prospective cohort study to correlate perceived sleep disturbances in depressed patients with objective changes in sleep architecture using polysomnography (PSG) before and after antidepressant therapy.Methods: Patients were recruited into the study after applying strict inclusion and exclusion criterion to rule out other comorbidities which could influence sleep. A diagnosis of Depressive episode was made based on ICD-10 DCR. Psychometry, in the form of Beck Depressive inventory (BDI) and HAMD (Hamilton depression rating scale) insomnia subscale was applied on Day 1 of admission. Patients were subjected to sleep study on Day 03 of admission with Polysomnography. Patients were started on antidepressant treatment post Polysomnography. An adequate trial of antidepressants for 08 weeks was administered and BDI score ≤09 was taken as remission. Polysomnography was repeated post remission. Statistical analysis was performed using Kruskal Wallis test and Pearson correlation coefficient.Results: The results showed positive (improvement) polysomnographic findings in terms of total sleep time, sleep efficiency, wake after sleep onset, percentage wake time and these findings were statistically significant. HAM-D Insomnia subscale was found to correlate with total sleep time, sleep efficiency, wake after sleep onset, total wake time and N2 Stage percentage.Conclusions: Antidepressant treatment effectively improves sleep architecture in Depressive disorder and HAM-D Insomnia subscale correlates with objective findings of total sleep time, sleep efficiency, wake after sleep onset, total wake time and duration of N2 stage of NREM.

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Sleep disturbance and quality of life in Rheumatoid Arthritis: a prospective mobile health study (Preprint)

10.2196/preprints.32825 ◽

2021 ◽

Author(s):

John McBeth ◽

William G Dixon ◽

Susan Mary Moore ◽

Bruce Hellman ◽

Ben James ◽

...

Keyword(s):

Quality Of Life ◽

Rheumatoid Arthritis ◽

Sleep Disturbance ◽

Mobile Health ◽

Sleep Disturbances ◽

Total Sleep Time ◽

Sleep Onset ◽

Sleep Time ◽

Domain Scores

BACKGROUND Sleep disturbance and poor health related quality of life (HRQoL) are common in people with rheumatoid arthritis (RA). Sleep disturbances, such as less total sleep time, more waking periods after sleep onset, and higher levels of non-restorative sleep, may be a driver of HRQoL. However, understanding if these sleep disturbances reduce HRQoL has, to date, been challenging due to the need to collect complex time-varying data in high resolution. Such data collection has now been made possible by the widespread availability and use of mobile health (mHealth) technologies. OBJECTIVE In a mobile health (mHealth) study we tested whether sleep disturbance (both absolute values and variability) caused poor HRQoL. METHODS The Quality of life, sleep and rheumatoid arthritis (QUASAR) study was a prospective mHealth study of adults with RA. Participants completed a baseline questionnaire, and for 30 days wore a triaxial accelerometer to objectively assess sleep, and provided daily reports via a smartphone app of sleep (Consensus Sleep Diary (CSD)), pain, fatigue, mood, and other symptoms. Participants completed the World Health Organization Quality of Life-Brief (WHOQoL-BREF) questionnaire every 10 days. Multi-level modelling tested the relationship between sleep variables and WHOQoL-BREF domains (physical, psychological, environment and social). RESULTS Of 268 recruited participants, 254 were included in this analysis. Across all WHOQoL-BREF domains, participant’s scores were lower than the population average. CSD sleep parameters predicted WHOQoL-BREF domain scores. For example, for each hour increase in the total time asleep physical domain scores increased by 1.11 points (β = 1.11 (0.07, 2.15)) and social domain scores increased by 1.65 points. These associations were not explained by sociodemographic and lifestyle factors, disease activity, medication use, levels of anxiety, sleep quality, or clinical sleep disorders. They were, however, attenuated and no longer significant when pain, fatigue and mood were included in the model. Increased variability in the total time asleep, was associated with poorer physical and psychological domain scores independently of all covariates. There were no patterns of association between actigraphy measured sleep and WHOQoL-BREF. CONCLUSIONS Optimising total sleep time, increasing sleep efficiency, decreasing sleep onset latency, and reducing the variability in total sleep time could improve HRQoL in people with RA.

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Validity, Reliability and Sensitivity to Change of Three Consumer-grade Activity Trackers in Controlled and Free-living Conditions among Older Adults (Preprint)

10.2196/preprints.27155 ◽

2021 ◽

Author(s):

Kaja Kastelic ◽

Marina Dobnik ◽

Stefan Loefler ◽

Christian Hofer ◽

Nejc Šarabon

Keyword(s):

Physical Activity ◽

Older Adults ◽

Intraclass Correlation ◽

Sleep Time ◽

Living Conditions ◽

Percentage Error ◽

Sensitivity To Change ◽

Free Living ◽

Activity Trackers ◽

Free Living Conditions

BACKGROUND Wrist worn consumer-grade activity trackers are popular devices, developed mainly for personal use, but with the potential to be used also for clinical and research purposes. OBJECTIVE The objective of this study was to explore the validity, reliability and sensitivity to change of movement behaviours metrics from three popular activity trackers (POLAR Vantage M, Garmin Vivosport and Garmin Vivoactive 4s) in controlled and free-living conditions when worn by older adults. METHODS Participants (n = 28; 74 ± 5 years) underwent a videotaped laboratory protocol while wearing all three activity trackers. On a separate occasion, participants wore one (randomly assigned) activity tracker and a research grade physical activity monitor ActiGraph wGT3X-BT simultaneously for six consecutive days for comparisons. RESULTS Both Garmin activity trackers showed excellent performance for step counts, with mean absolute percentage error (MAPE) below 20 % and intraclass correlation coefficient (ICC2,1) above 0.90 (P < .05), while Polar Vantage M substantially over counted steps (MAPE = 84 % and ICC2,1 = 0.37 for free-living conditions). MAPE for sleep time was within 10 % for all the trackers tested, while far beyond 20 % for all the physical activity and calories burned outputs. Both Garmin trackers showed fair agreement (ICC2,1 = 0.58–0.55) for measuring calories burned when compared with ActiGraph. CONCLUSIONS Garmin Vivoactive 4s showed overall best performance, especially for measuring steps and sleep time in healthy older adults. Minimal detectible change was consistently lower for an average day measures than for a single day measure, but still relatively high. The results provided in this study could be used to guide choice on activity trackers aiming for different purposes – individual use/care, longitudinal monitoring or in clinical trial setting.

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1059 Sleep Parameters In People With Type 2 Diabetes With And Without Insomnia Symptoms

SLEEP ◽

10.1093/sleep/zsaa056.1055 ◽

2020 ◽

Vol 43 (Supplement_1) ◽

pp. A402-A403

Author(s):

M Alshehri ◽

A Alkathiry ◽

A Alenazi ◽

S Alothman ◽

J Rucker ◽

...

Keyword(s):

Type 2 Diabetes ◽

Total Sleep Time ◽

Sleep Onset ◽

Sleep Time ◽

Circular Data ◽

Objective Measures ◽

Subjective Measures ◽

Sleep Parameters ◽

Insomnia Symptoms

Abstract Introduction There is an increasing awareness of the high prevalence of insomnia symptoms in people with type 2 diabetes (T2D). Past studies have demonstrated the importance of measuring sleep parameters in both averages and variabilities using subjective and objective methods. Thus, we aimed to compare the averages and variability of sleep parameters in people with T2D with and without insomnia symptoms. Methods Actigraph measurements and sleep diaries were used in 59 participants to assess sleep parameters, including sleep efficiency (SE), sleep latency, total sleep time, and wake after sleep onset over seven nights. Validated instruments were used to assess the symptoms of depression, anxiety, and pain. Circular data were used to describe the distribution of bed distribution with SE as a magnitude for both groups. Mann Whitney U test was utilized to compare averages and variability of sleep parameters between the two groups. Multivariable general linear model to control for demographic and clinical variables. For the secondary aim, multiple linear regression tests were utilized to assess the association between averages and variability values for both groups. Results SE was found to be lower in average and higher in variability for participants with T2D and insomnia symptoms, than those with T2D only subjectively and objectively. SE variability was also the only sleep parameter higher in people with T2D and insomnia symptoms, with psychological symptoms potentially playing a role in this difference. We observed that people in T2D+Insomnia tend to go to bed earlier compared to the T2D only group based on objective measures, but no difference was observed between groups in subjective measures. The only significant relationship in both objective and subjective measures was between the averages and variability of SE. Conclusion Our findings suggest a discrepancy between subjective and objective measures in only average of total sleep time, as well as agreement in measures of variability in sleep parameters. Also, the relationship between averages and variabilities suggested the importance of improving SE to minimize its variability. Further research is warranted to investigate the complex relationship between sleep parameters and psychological factors in people with T2D and insomnia symptoms. Support None

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A Comparative Study of Sleep and Mood Between Young Elite Athletes and Age-Matched Controls

Journal of Physical Activity and Health ◽

10.1123/jpah.2016-0513 ◽

2017 ◽

Vol 14 (6) ◽

pp. 465-473 ◽

Cited By ~ 6

Author(s):

Anette Harris ◽

Hilde Gundersen ◽

Pia Mørk Andreassen ◽

Eirunn Thun ◽

Bjørn Bjorvatn ◽

...

Keyword(s):

Physical Activity ◽

Total Sleep Time ◽

Elite Athletes ◽

Sleep Onset ◽

Sleep Time ◽

Sleep Diary ◽

Sleep Onset Latency ◽

Onset Latency ◽

Social Jetlag ◽

Weekly Training

Background:Sleep and mood have seldom been compared between elite athletes and nonelite athletes, although potential differences suggest that physical activity may affect these parameters. This study aims to explore whether adolescent elite athletes differ from controls in terms of sleep, positive affect (PA) and negative affect (NA).Methods:Forty-eight elite athletes and 26 controls participating in organized and nonorganized sport completed a questionnaire, and a 7-day sleep diary.Results:On school days, the athletes and the controls who participated in organized and nonorganized sport differed in bedtime (22:46, 23:14, 23:42, P < .01), sleep onset (23:03, 23:27, 00:12, P < .01), and total sleep time (7:52, 8:00, 6:50, P < 01). During weekend, the athletes, the controls who participated in organized and nonorganized sport differed in bedtime (23:30, 00:04, 00:49, P < .01), sleep onset (23.42, 00:18, 01:13, P < .01), rise time (9:15, 9:47, 10:55, P < .01), sleep efficiency (95.0%, 94.2%, 90.0%, P < 05), and sleep onset latency (11.8, 18.0, 28.0 minutes, P < .01). Furthermore, the athletes reported less social jetlag (0:53) and higher score for PA (34.3) compared with the controls who participated in nonorganized sport (jetlag: 1:25, P < .05, PA: 29.8, P < .05).Conclusions:An almost dose-response association was found between weekly training hours, sleep, social jetlag and mood in adolescents.

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Association Between Electroencephalogram-Derived Sleep Measures and the Change of Emotional Status Analyzed Using Voice Patterns: Observational Pilot Study

JMIR Formative Research ◽

10.2196/16880 ◽

2020 ◽

Vol 4 (6) ◽

pp. e16880

Author(s):

Hirotaka Miyashita ◽

Mitsuteru Nakamura ◽

Akiko Kishi Svensson ◽

Masahiro Nakamura ◽

Shinichi Tokuno ◽

...

Keyword(s):

Pilot Study ◽

Regression Coefficient ◽

Total Sleep Time ◽

Sleep Onset ◽

Sleep Time ◽

Sleep Onset Latency ◽

Inverse Association ◽

Onset Latency ◽

Emotional Status ◽

Nighttime Sleep

Background Measuring emotional status objectively is challenging, but voice pattern analysis has been reported to be useful in the study of emotion. Objective The purpose of this pilot study was to investigate the association between specific sleep measures and the change of emotional status based on voice patterns measured before and after nighttime sleep. Methods A total of 20 volunteers were recruited. Their objective sleep measures were obtained using a portable single-channel electroencephalogram system, and their emotional status was assessed using MIMOSYS, a smartphone app analyzing voice patterns. The study analyzed 73 sleep episodes from 18 participants for the association between the change of emotional status following nighttime sleep (Δvitality) and specific sleep measures. Results A significant association was identified between total sleep time and Δvitality (regression coefficient: 0.036, P=.008). A significant inverse association was also found between sleep onset latency and Δvitality (regression coefficient: –0.026, P=.001). There was no significant association between Δvitality and sleep efficiency or number of awakenings. Conclusions Total sleep time and sleep onset latency are significantly associated with Δvitality, which indicates a change of emotional status following nighttime sleep. This is the first study to report the association between the emotional status assessed using voice pattern and specific sleep measures.

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The feasibility and reliability of actigraphy to monitor sleep in intensive care patients: An observational study

10.21203/rs.3.rs-66768/v1 ◽

2020 ◽

Author(s):

L.J. Delaney ◽

E. Litton ◽

K.L Melehan ◽

H-C.C Huang ◽

V Lopez ◽

...

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Observational Study ◽

Total Sleep Time ◽

Sleep Time ◽

Moderate Level ◽

Sleep Monitoring ◽

Clinical Trial Registration ◽

Wake Time ◽

Intensive Care Patients

Abstract Background: Sleep amongst intensive care patients is reduced and highly fragmented which may adversely impact on recovery. The current challenge for Intensive Care clinicians is identifying feasible and accurate assessments of sleep that can be widely implemented. The objective of this study was to investigate the feasibility and reliability of a minimally invasive sleep monitoring technique compared to the gold standard, polysomnography, for sleep monitoring. Methods: Prospective observational study employing a within subject design in adult patients admitted to an Intensive Care Unit. Sleep monitoring was undertaken amongst minimally sedated patients via concurrent polysomnography and actigraphy monitoring over a 24-hour duration to assess agreement between the two methods; total sleep time and wake time. Results: We recruited 80 patients who were mechanically ventilated (24%) and non-ventilated (76%) within the intensive care unit. Sleep was found to be highly fragmented, composed of numerous sleep bouts and characterized by abnormal sleep architecture. Actigraphy was found to have a moderate level of overall agreement in identifying sleep and wake states with polysomnography (69.4%; K = 0.386, p < 0.05) in an epoch by epoch analysis, with a moderate level of sensitivity (65.5%) and specificity (76.1%). Monitoring accuracy via actigraphy was improved amongst non-ventilated patients (specificity 83.7%; sensitivity 56.7%). Actigraphy was found to have a moderate correlation with polysomnography reported total sleep time (r = 0.359, p < 0.05) and wakefulness (r = 0.371 p < 0.05). Bland-Altman plots indicated that sleep was underestimated by actigraphy, with wakeful states overestimated. Conclusions: Actigraphy was easy and safe to use, provided moderate level of agreement with polysomnography in distinguishing between sleep and wakeful states, and may be a reasonable alternative to measure sleep in intensive care patients. Clinical Trial Registration number: ACTRN12615000945527 (Registered 9/9/2015)

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