A novel approach using actigraphy to quantify the level of disruption of sleep by in-home polysomnography: the MrOS Sleep Study: Sleep disruption by polysomnography.
ABSTRACT: The "first-night effect" of polysomnography (PSG) has been previously studied; however, the ability to quantify the sleep disruption level has been confounded with the use of PSG on all nights. We used actigraphy to quantify disruption level and examined characteristics associated with disruption.Totally, 778 older men (76.2 ± 5.4 years) from a population-based study at six US centers underwent one night of in-home PSG. Actigraphy was performed on the PSG night and three subsequent nights. Actigraphically measured total sleep time (TST), sleep efficiency (SE), wake after sleep onset (WASO), and sleep onset latency (SOL) from the PSG night and subsequent nights were compared. Linear regression models were used to examine the association of characteristics and sleep disruption.On average, sleep on the PSG night was worse than the following night (p < 0.05, TST 21 ± 85 min less, SE 2.3 ± 11.3% less, WASO 4.9 ± 51.8 min more, SOL 6.6 ± 56.2 min more). Sleep on the PSG night was significantly worse than that two and three nights later. Characteristics associated with greater sleep disruption on the PSG night included older age, higher apnea-hypopnea index, worse neuromuscular function, and more depressive symptoms. Minorities and men with excessive daytime sleepiness slept somewhat better on the PSG night.Among older men, there was sleep disruption on the PSG night, which may lead to sleep time underestimation. The increase of sleep on the night after the PSG suggests that data from the second monitoring may overestimate sleep.
Project description:We validated actigraphy for detecting sleep and wakefulness versus polysomnography (PSG).Actigraphy and polysomnography were simultaneously collected during sleep laboratory admissions. All studies involved 8.5 h time in bed, except for sleep restriction studies. Epochs (30-sec; n = 232,849) were characterized for sensitivity (actigraphy = sleep when PSG = sleep), specificity (actigraphy = wake when PSG = wake), and accuracy (total proportion correct); the amount of wakefulness after sleep onset (WASO) was also assessed. A generalized estimating equation (GEE) model included age, gender, insomnia diagnosis, and daytime/nighttime sleep timing factors.Controlled sleep laboratory conditions.Young and older adults, healthy or chronic primary insomniac (PI) patients, and daytime sleep of 23 night-workers (n = 77, age 35.0 ± 12.5, 30F, mean nights = 3.2).N/A.Overall, sensitivity (0.965) and accuracy (0.863) were high, whereas specificity (0.329) was low; each was only slightly modified by gender, insomnia, day/night sleep timing (magnitude of change < 0.04). Increasing age slightly reduced specificity. Mean WASO/night was 49.1 min by PSG compared to 36.8 min/night by actigraphy (? = 0.81; CI = 0.42, 1.21), unbiased when WASO < 30 min/night, and overestimated when WASO > 30 min/night.This validation quantifies strengths and weaknesses of actigraphy as a tool measuring sleep in clinical and population studies. Overall, the participant-specific accuracy is relatively high, and for most participants, above 80%. We validate this finding across multiple nights and a variety of adults across much of the young to midlife years, in both men and women, in those with and without insomnia, and in 77 participants. We conclude that actigraphy is overall a useful and valid means for estimating total sleep time and wakefulness after sleep onset in field and workplace studies, with some limitations in specificity.
Project description:STUDY OBJECTIVES:To compare the quality and consistency in sleep measurement of a consumer wearable device and a research-grade actigraph with polysomnography (PSG) in adolescents. METHODS:Fifty-eight healthy adolescents (aged 15-19 years; 30 males) underwent overnight PSG while wearing both a Fitbit Alta HR and a Philips Respironics Actiwatch 2 (AW2) for 5 nights, with either 5 hours or 6.5 hours time in bed (TIB) and for 4 nights with 9 hours TIB. AW2 data were evaluated using two different wake and immobility thresholds. Discrepancies in estimated total sleep time (TST) and wake after sleep onset (WASO) between devices and PSG, as well as epoch-by-epoch agreements in sleep/wake classification, were assessed. Fitbit-generated sleep staging was compared to PSG. RESULTS:Fitbit and AW2 under default settings similarly underestimated TST and overestimated WASO (TST: medium setting (M10) ? 38 minutes, Fitbit ? 47 minutes; WASO: M10 ? 38 minutes; Fitbit ? 42 minutes). AW2 at the high motion threshold setting provided readings closest to PSG (TST: ? 12 minutes; WASO: ? 18 minutes). Sensitivity for detecting sleep was ? 90% for both wearable devices and further improved to 95% by using the high threshold (H5) setting for the AW2 (0.95). Wake detection specificity was highest in Fitbit (? 0.88), followed by the AW2 at M10 (? 0.80) and H5 thresholds (? 0.73). In addition, Fitbit inconsistently estimated stage N1 + N2 sleep depending on TIB, underestimated stage N3 sleep (21-46 min), but was comparable to PSG for rapid eye movement sleep. Fitbit sensitivity values for the detection of N1 + N2, N3 and rapid eye movement sleep were ? 0.68, ? 0.50, and ? 0.72, respectively. CONCLUSIONS:A consumer-grade wearable device can measure sleep duration as well as a research actigraph. However, sleep staging would benefit from further refinement before these methods can be reliably used for adolescents. CLINICAL TRIAL REGISTRATION:Registry: ClinicalTrials.gov; Title: The Cognitive and Metabolic Effects of Sleep Restriction in Adolescents; Identifier: NCT03333512; URL: https://clinicaltrials.gov/ct2/show/NCT03333512. CITATION:Lee XK, Chee NIYN, Ong JL, Teo TB, van Rijn E, Lo JC, Chee MWL. Validation of a consumer sleep wearable device with actigraphy and polysomnography in adolescents across sleep opportunity manipulations. J Clin Sleep Med. 2019;15(9):1337-1346.
Project description:Actigraphy is commonly used to measure sleep outcomes so that sleep can be measured conveniently at home over multiple nights. Actigraphy has been validated in people with sleep disturbances; however, the validity of scoring settings in people with chronic medical illnesses such as chronic obstructive pulmonary disease remains unclear. The purpose of this secondary analysis was to compare actigraphy-customized scoring settings with polysomnography (PSG) for the measurement of sleep outcomes in people with chronic obstructive pulmonary disease who have insomnia.Participants underwent overnight sleep assessment simultaneously by PSG and actigraphy at the University of Illinois of Chicago Sleep Science Center. Fifty participants (35 men and 15 women) with mild-to-severe chronic obstructive pulmonary disease and co-existing insomnia were included in the analysis. Sleep onset latency, total sleep time (TST), wake after sleep onset (WASO), and sleep efficiency (SE) were calculated independently from data derived from PSG and actigraphy. Actigraphy sleep outcome scores obtained at the default setting and several customized actigraphy settings were compared to the scored PSG results.Although no single setting was optimal for all sleep outcomes, the combination of 10 consecutive immobile minutes for sleep onset or end and an activity threshold of 10 worked well. Actigraphy overestimated TST and SE and underestimated WASO, but there was no difference in variance between PSG and actigraphy in TST and SE when the 10 × 10 combination was used. As the average TST and SE increased, the agreement between PSG and actigraphy appeared to increase, and as the average WASO decreased, the agreement between PSG and actigraphy appeared to increase.Results support the conclusion that the default actigraphy settings may not be optimal for people with chronic obstructive pulmonary disease and co-existing insomnia.
Project description:Actigraphy (ACT) is a non-invasive objective assessment tool for the study of sleep-wake rhythms. It is of particular interest in children with autism spectrum disorder (ASD), as sleep disorders are highly prevalent and have a significant impact on both cognitive and behavioral functions. As polysomnography (PSG), the gold standard for the assessment of sleep, is difficult to perform in children with ASD, ACT has become a tool of choice but has not yet been validated against PSG using state-of-the-art methodology. The main objective of this study was to assess, for the first time, the validity of ACT compared to PSG for the measurement of sleep in children with ASD. During the same night of hospitalization, PSG and ACT were conducted in 26 children (6 girls and 20 boys; mean age 5.4 years ± 1.6) diagnosed with ASD according to DSM-5 criteria and standardized diagnostic scales. Sleep parameters were total sleep time (TST), sleep latency (SL), wake after sleep onset (WASO), and sleep efficiency (SE). To compare PSG and ACT, we conducted sleep parameter agreement analyses including: intraclass correlation coefficient (ICC), Bland-Altman plots, and equivalence tests. The comparison also included an epoch-by-epoch (EBE) agreement analysis to determine sensitivity (ability to detect sleep) and specificity (ability to detect wake). According to equivalence tests, the difference between ACT and PSG measures was clinically acceptable for TST (<30 min, p < 0.01), SL (<15 min, p < 0.001), and SE (10%, p < 0.01), but not for WASO (<15 min, p = 0.13). There was a good agreement between methods for SL (ICC = 0.79) and TST (ICC = 0.85) and a moderate agreement for WASO (ICC = 0.73) and SE (ICC = 0.68). The EBE agreement analysis revealed a high sensitivity (0.94 ± 0.06) and moderate specificity (0.5 ± 0.2). Since sleep disorders are one of the most common comorbidities within the ASD population and are highly prevalent, it is essential to validate objective tools of assessment. To our knowledge, our study is the first to validate ACT compared to PSG, using a state-of-the-art methodology, in children with ASD. The results suggest ACT to be a valid method to evaluate sleep within this population, with a good reliability for most sleep parameters.
Project description:<h4>Study objectives</h4>Assess the short- and long-term stability of sleep duration in patients with insomnia and normal-sleeping controls.<h4>Design</h4>Observational short-term and prospective studies.<h4>Setting</h4>Sleep laboratory.<h4>Participants</h4>Patients with insomnia (n = 150) and controls (n = 151) were recruited from the local community or sleep disorders clinic. A subsample of 95 men from the Penn State Adult Cohort (PSAC) were followed up 2.6 y after their initial visit.<h4>Measurements</h4>Participants underwent a physical examination and 8-h polysomnography (PSG) recording for 3 consecutive nights (controls and insomniacs), or 2 single nights separated by several years (PSAC). Intraclass correlation coefficients (ICCs) assessed the stability of the variables total sleep time (TST), sleep onset latency (SOL), and wake after sleep onset (WASO). We also examined persistence of the first-night classification of "short" versus "normal" sleep duration on subsequent nights.<h4>Results</h4>Stability of TST, SOL, and WASO based on 1 night were slight to moderate in both patients with insomnia (ICC = 0.37-0.57) and controls (ICC = 0.39-0.59), and became substantial to almost perfect when based on the average of 3 nights (ICC = 0.64-0.81). We observed similar degrees of stability for TST and WASO in the longitudinal sample, with moderate stability based on a single night and substantial stability based on both nights. In examining the persistence of "short" and "normal" sleep duration, 71.4% (controls), 74.7% (patients with insomnia), and 72.6% (longitudinal sample) of participants retained their first-night classifications over subsequent nights.<h4>Conclusions</h4>Sleep duration variables, particularly total sleep time based on 3 consecutive nights in both patients with insomnia and controls or two single-night recordings separated by several years, are stable and reflect a person's habitual sleep. Furthermore, a single night in the laboratory may be useful for reliably classifying one's sleep duration.
Project description:Objectives: Our count-scaled algorithm automatically scores sleep across 24 hours to process sleep timing, quantity, and quality. The aim of this study was to validate the algorithm against overnight PSG in children to determine the best site placement for sleep. Methods: 28 children (5-8 years) with no history of sleep disturbance wore two types of accelerometers (ActiGraph GT3X+ and Actical) at two sites (left hip, non-dominant wrist) for 24-h. Data were processed using the count-scaled algorithm. PSG data were collected using an in-home Type 2 device. PSG-actigraphy epoch sensitivity (sleep agreement) and specificity (wake agreement) were determined and sleep outcomes compared for timing (onset and offset), quantity [sleep period time (SPT) and total sleep time (TST)], and quality metrics [sleep efficiency and waking after sleep onset (WASO)]. Results: Overall, sensitivities were high (89.1% to 99.5%) and specificities low (21.1% to 45.7%). Sleep offset was accurately measured by actigraphy, regardless of brand or placement site. By contrast, sleep onset agreed with PSG using hip-positioned but not wrist-positioned devices (difference ActiGraph : PSG 21 min, P < .001; Actical : PSG 14 min, P < .001). The ActiGraph at the wrist accurately detected WASO and sleep efficiency, but under (-34 min, P < .001) and overestimated (5.8%, P < .001) these at the hip. The Actical under- and over-estimated these variables respectively at both sites. Results for TST varied ranging from significant differences to PSG of -26 to 21 min (ActiGraph wrist and hip respectively) and 9 min (ns) to 59 min for Actical (wrist and hip respectively). Conclusion: Overall the count-scaled algorithm produced high sensitivity at the expense of low specificity in comparison with PSG. A best site placement for estimates of all sleep variables could not be determined, but overall the results suggested ActiGraph GT3X+ at the hip may be superior for sleep timing and quantity metrics, whereas the wrist may be superior for sleep quality metrics. Both devices placed at the hip performed well for sleep timing but not for sleep quality. Differences are likely linked to freedom of movement of the wrist vs the trunk (hip) during overnight sleep.
Project description:In insomnia, actigraphy tends to underestimate wake time compared to diaries and PSG. When chronic pain co-occurs with insomnia, sleep may be more fragmented, including more movement and arousals. However, individuals may not be consciously aware of these arousals. We examined the baseline concordance of diaries, actigraphy, and PSG as well as the ability of each assessment method to detect changes in sleep following cognitive behavioral therapy for insomnia (CBT-I).Adults with insomnia and fibromyalgia (n = 113) were randomized to CBT-I, CBT for pain, or waitlist control. At baseline and posttreatment, participants completed one night of PSG and two weeks of diaries/actigraphy.At baseline, objective measures estimated lower SOL, higher TST, and higher SE than diaries (ps < 0.05). Compared to PSG, actigraphic estimates were higher for SOL and lower for WASO (ps < 0.05). Repeated measures ANOVAs were conducted for the CBT-I group (n = 15), and significant method by time interactions indicated that the assessment methods differed in their sensitivity to detect treatment-related changes. PSG values did not change significantly for any sleep parameters. However, diaries showed improvements in SOL, WASO, and SE, and actigraphy also detected the WASO and SE improvements (ps < 0.05).Actigraphy was generally more concordant with PSG than with diaries, which are the recommended assessment for diagnosing insomnia. However, actigraphy showed greater sensitivity to treatment-related changes than PSG; PSG failed to detect any improvements, but actigraphy demonstrated changes in WASO and SE, which were also found with diaries. In comorbid insomnia/fibromyalgia, actigraphy may therefore have utility in measuring treatment outcomes.
Project description:Wearable sleep technology allows for a less intruding sleep assessment than PSG, especially in long-term sleep monitoring. Though such devices are less accurate than PSG, sleep trackers may still provide valuable information. This study aimed to validate a commercial sleep tracker, Garmin Vivosmart 4 (GV4), against polysomnography (PSG) and to evaluate intra-device reliability (GV4 vs. GV4). Eighteen able-bodied adults (13 females, M = 56.1 ± 12.0 years) with no self-reported sleep disorders were simultaneously sleep monitored by GV4 and PSG for one night while intra-device reliability was monitored in one participant for 23 consecutive nights. Intra-device agreement was considered sufficient (observed agreement = 0.85 ± 0.13, Cohen's kappa = 0.68 ± 0.24). GV4 detected sleep with high accuracy (0.90) and sensitivity (0.98) but low specificity (0.28). Cohen's kappa was calculated for sleep/wake detection (0.33) and sleep stage detection (0.20). GV4 significantly underestimated time awake (p = 0.001) including wake after sleep onset (WASO) (p = 0.001), and overestimated light sleep (p = 0.045) and total sleep time (TST) (p = 0.001) (paired t-test). Sleep onset and sleep end differed insignificantly from PSG values. Our results suggest that GV4 is not able to reliably describe sleep architecture but may allow for detection of changes in sleep onset, sleep end, and TST (ICC ? 0.825) in longitudinally followed groups. Still, generalizations are difficult due to our sample limitations.
Project description:STUDY OBJECTIVES:Individuals with primary insomnia often have poorer self-reported sleep than objectively measured sleep, a phenomenon termed negative sleep discrepancy. Recent studies suggest that this phenomenon might differ depending on comorbidities. This study examined sleep discrepancy, its night-to-night variability, and its correlates in comorbid insomnia and fibromyalgia. METHODS:Sleep diaries and actigraphy data were obtained from 223 adults with fibromyalgia and insomnia (age = 51.53 [standard deviation = 11.90] years; 93% women) for 14 days. Sleep discrepancy was calculated by subtracting diary from actigraphy estimates of sleep onset latency (SOL-D), wake after sleep onset (WASO-D), and total sleep time (TST-D) for each night. Night-to-night variability in sleep discrepancy was calculated by taking the within-individual standard deviations over 14 days. Participants completed measures of mood, pain, fatigue, sleep/pain medications, nap duration, and caffeine consumption. RESULTS:Average sleep discrepancies across 14 days were small for all sleep parameters (< 10 minutes). There was no consistent positive or negative discrepancy. However, sleep discrepancy for any single night was large, with average absolute discrepancies greater than 30 minutes for all sleep parameters. Greater morning pain was associated with larger previous-night WASO-D, although diary and actigraphy estimates of WASO remained fairly concordant. Taking prescribed pain medications, primarily opioids, was associated with greater night-to-night variability in WASO-D and TST-D. CONCLUSIONS:Unlike patients with primary insomnia, patients with comorbid fibromyalgia do not exhibit consistent negative sleep discrepancy; however, there are both substantial positive and negative discrepancies in all sleep parameters at the daily level. Future research is needed to investigate the clinical significance and implications of high night-to-night variability of sleep discrepancy, and the role of prescribed opioid medications in sleep perception.
Project description:To validate a contact-free system designed to achieve maximal comfort during long-term sleep monitoring, together with high monitoring accuracy.We used a contact-free monitoring system (EarlySense, Ltd., Israel), comprising an under-the-mattress piezoelectric sensor and a smartphone application, to collect vital signs and analyze sleep. Heart rate (HR), respiratory rate (RR), body movement, and calculated sleep-related parameters from the EarlySense (ES) sensor were compared to data simultaneously generated by the gold standard, polysomnography (PSG). Subjects in the sleep laboratory underwent overnight technician-attended full PSG, whereas subjects at home were recorded for 1 to 3 nights with portable partial PSG devices. Data were compared epoch by epoch.A total of 63 subjects (85 nights) were recorded under a variety of sleep conditions. Compared to PSG, the contact-free system showed similar values for average total sleep time (TST), % wake, % rapid eye movement, and % non-rapid eye movement sleep, with 96.1% and 93.3% accuracy of continuous measurement of HR and RR, respectively. We found a linear correlation between TST measured by the sensor and TST determined by PSG, with a coefficient of 0.98 (R = 0.87). Epoch-by-epoch comparison with PSG in the sleep laboratory setting revealed that the system showed sleep detection sensitivity, specificity, and accuracy of 92.5%, 80.4%, and 90.5%, respectively.TST estimates with the contact-free sleep monitoring system were closely correlated with the gold-standard reference. This system shows good sleep staging capability with improved performance over accelerometer-based apps, and collects additional physiological information on heart rate and respiratory rate.