ActiGraph GT3X+ and Actical Wrist and Hip Worn Accelerometers for Sleep and Wake Indices in Young Children Using an Automated Algorithm: Validation With Polysomnography.
ABSTRACT: 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:Improving and validating sleep scoring algorithms for actigraphs enhances their usefulness in clinical and research applications. The MTI(®) device (ActiGraph, Pensacola, FL) had not been previously validated for sleep. The aims were to (1) compare the accuracy of sleep metrics obtained via wrist- and hip-mounted MTI(®) actigraphs with polysomnographic (PSG) recordings in a sample that included both normal sleepers and individuals with presumed sleep disorders; and (2) develop a novel sleep scoring algorithm using spline regression to improve the correspondence between the actigraphs and PSG.Original actigraphy data were amplified and their pattern was estimated using a penalized spline. The magnitude of amplification and the spline were estimated by minimizing the difference in sleep efficiency between wrist- (hip-) actigraphs and PSG recordings. Sleep measures using both the original and spline-modified actigraphy data were compared to PSG using the following: mean sleep summary measures; Spearman rank-order correlations of summary measures; percent of minute-by-minute agreement; sensitivity and specificity; and Bland-Altman plots.The original wrist actigraphy data showed modest correspondence with PSG, and much less correspondence was found between hip actigraphy and PSG. The spline-modified wrist actigraphy produced better approximations of interclass correlations, sensitivity, and mean sleep summary measures relative to PSG than the original wrist actigraphy data. The spline-modified hip actigraphy provided improved correspondence, but sleep measures were still not representative of PSG.The results indicate that with some refinement, the spline regression method has the potential to improve sleep estimates obtained using wrist actigraphy.
Project description:BACKGROUND:Actigraphs are wrist-worn devices that record tri-axial accelerometry data used clinically and in research studies. The expense of research-grade actigraphs, however, limit their widespread adoption, especially in clinical settings. Tri-axial accelerometer-based consumer wearable devices have gained worldwide popularity and hold potential for a cost-effective alternative. The lack of independent validation of minute-to-minute accelerometer data with polysomnographic data or even research-grade actigraphs, as well as access to raw data has hindered the utility and acceptance of consumer-grade actigraphs. METHODS:Sleep clinic patients wore a consumer-grade wearable (Huami Arc) on their non-dominant wrist while undergoing an overnight polysomnography (PSG) study. The sample was split into two, 20 in a training group and 21 in a testing group. In addition to the Arc, the testing group also wore a research-grade actigraph (Philips Actiwatch Spectrum). Sleep was scored for each 60-s epoch on both devices using the Cole-Kripke algorithm. RESULTS:Based on analysis of our training group, Arc and PSG data were aligned best when a threshold of 10 units was used to examine the Arc data. Using this threshold value in our testing group, the Arc has an accuracy of 90.3%±4.3%, sleep sensitivity (or wake specificity) of 95.5%±3.5%, and sleep specificity (wake sensitivity) of 55.6%±22.7%. Compared to PSG, Actiwatch has an accuracy of 88.7%±4.5%, sleep sensitivity of 92.6%±5.2%, and sleep specificity of 60.5%±20.2%, comparable to that observed in the Arc. CONCLUSIONS:An optimized sleep/wake threshold value was identified for a consumer-grade wearable Arc trained by PSG data. By applying this sleep/wake threshold value for Arc generated accelerometer data, when compared to PSG, sleep and wake estimates were adequate and comparable to those generated by a clinical-grade actigraph. As with other actigraphs, sleep specificity plateaus due to limitations in distinguishing wake without movement from sleep. Further studies are needed to evaluate the Arc's ability to differentiate between sleep and wake using other sources of data available from the Arc, such as high resolution accelerometry and photoplethysmography.
Project description:This study aimed to examine the validity and accuracy of wrist accelerometers for classifying sedentary behavior (SB) in children.Fifty-seven children (5-8 and 9-12 yr) completed an ~170-min protocol, including 15 semistructured activities and transitions. Nine ActiGraph (GT3X+) and two GENEActiv wrist cut points were evaluated. Direct observation was the criterion measure. The accuracy of wrist cut points was compared with that achieved by the ActiGraph hip cut point (?25 counts per 15 s) and the thigh-mounted activPAL3. Analyses included equivalence testing, Bland-Altman procedures, and area under the receiver operating curve (ROC-AUC).The most accurate ActiGraph wrist cut points (Kim; vector magnitude, ?3958 counts per 60 s; vertical axis, ?1756 counts per 60 s) demonstrated good classification accuracy (ROC-AUC = 0.85-0.86) and accurately estimated SB time in 5-8 yr (equivalence P = 0.02; mean bias = 4.1%, limits of agreement = -20.1% to 28.4%) and 9-12 yr (equivalence P < 0.01; -2.5%, -27.9% to 22.9%). The mean bias of SB time estimates from Kim were smaller than ActiGraph hip (5-8 yr: 15.8%, -5.7% to 37.2%; 9-12 yr: 17.8%, -3.9% to 39.5%) and similar to or smaller than activPAL3 (5-8 yr: 12.6%, -39.8% to 14.7%; 9-12 yr: -1.4%, -13.9% to 11.0%), although classification accuracy was similar to ActiGraph hip (ROC-AUC = 0.85) but lower than activPAL3 (ROC-AUC = 0.92-0.97). Mean bias (5-8 yr: 6.5%, -16.1% to 29.1%; 9-12 yr: 10.5%, -13.6% to 34.6%) for the most accurate GENEActiv wrist cut point (Schaefer: ?0.19 g) was smaller than ActiGraph hip, and activPAL3 in 5-8 yr, but larger than activPAL3 in 9-12 yr. However, SB time estimates from Schaefer were not equivalent to direct observation (equivalence P > 0.05) and classification accuracy (ROC-AUC = 0.79-0.80) was lower than for ActiGraph hip and activPAL3.The most accurate SB ActiGraph (Kim) and GENEActiv (Schaefer) wrist cut points can be applied in children with similar confidence as the ActiGraph hip cut point (?25 counts per 15 s), although activPAL3 was generally more accurate.
Project description:BACKGROUND:Activity trackers such as the Fitbit Charge 2 enable users and researchers to monitor physical activity in daily life, which could be beneficial for changing behaviour. However, the accuracy of the Fitbit Charge 2 in a free-living environment is largely unknown. OBJECTIVE:To investigate the agreement between Fitbit Charge 2 and ActiGraph GT3X for the estimation of steps, energy expenditure, time in sedentary behaviour, and light and moderate-to-vigorous physical activity under free-living conditions, and further examine to what extent placing the ActiGraph on the wrist as opposed to the hip would affect the findings. METHODS:41 adults (n = 10 males, n = 31 females) were asked to wear a Fitbit Charge 2 device and two ActiGraph GT3X devices (one on the hip and one on the wrist) for seven consecutive days and fill out a log of wear times. Agreement was assessed through Bland-Altman plots combined with multilevel analysis. RESULTS:The Fitbit measured 1,492 steps/day more than the hip-worn ActiGraph (limits of agreement [LoA] = -2,250; 5,234), while for sedentary time, it measured 25 min/day less (LoA = -137; 87). Both Bland-Altman plots showed fixed bias. For time in light physical activity, the Fitbit measured 59 min/day more (LoA = -52;169). For time in moderate-to-vigorous physical activity, the Fitbit measured 31 min/day less (LoA = -132; 71) and for activity energy expenditure it measured 408 kcal/day more than the hip-worn ActiGraph (LoA = -385; 1,200). For the two latter outputs, the plots indicated proportional bias. Similar or more pronounced discrepancies, mostly in opposite direction, appeared when comparing to the wrist-worn ActiGraph. CONCLUSION:Moderate to substantial differences between devices were found for most outputs, which could be due to differences in algorithms. Caution should be taken if replacing one device with another and when comparing results.
Project description:STUDY OBJECTIVES:Sleep problems are often undetected in adults with Down syndrome (DS). Our objective was to determine the prevalence of sleep disorders in adults with DS through self-reported and objective sleep measures. METHODS:We performed a community-based cross-sectional study of 54 adults with DS not referred for sleep disorders. Two polysomnography (PSG) sleep studies were performed. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI); daytime sleepiness was evaluated using the Epworth Sleepiness Scale (ESS) and the risk for the sleep apnea syndrome (OSA) was identified using the Berlin Questionnaire (BQ). Participants' sleep/wake pattern was assessed from sleep diaries and by wrist actigraphy. PSQI, ESS, and PSG measures were compared with 35 sex-, age-, and body mass index-matched patients in the control groups. RESULTS:In PSG measures, adults with DS showed lower sleep efficiency (69 ± 17.7 versus 81.6 ± 11; P < .001), less rapid eye movement sleep (9.4 ± 5.8 versus 19.4 ± 5.1; P < .001), a higher prevalence of OSA (78% versus 14%; P < .001), and a higher apnea-hypopnea index (23.5 ± 24.5 versus 3.8 ± 10.5; P < .001) than patients in the control group. In the DS group, the questionnaires (mean PSQI 3.7 ± 2.9; mean ESS 6.3 ± 4.5 and mean BQ 1 ± 0) did not reflect the sleep disturbances detected on the PSG. Actigraphy data recorded daytime sleep that was not self-reported (118.2 ± 104.2 minutes). CONCLUSIONS:Adults with DS show severe sleep disruption and a high prevalence of OSA, undetected by self-reported sleep measures. Actigraphy, PSG, and validated simplified devices for screening OSA should be routinely recommended for this population because treatment of sleep disorders can contribute to healthy aging.
Project description:Wrist actigraphy (ACT) may overestimate sleep and underestimate wake, and the agreement may be lower in people with chronic conditions who often have poor sleep and low activity levels. The purpose of this systematic review is to compare the agreement between ACT and polysomnographic (PSG) measures of sleep in adults without chronic conditions and sleep complaints (healthy) and with chronic conditions. We conducted a systematic review and meta-analysis using PRISMA guidelines. We searched PubMed, OVIDEMBASE, OVIDMEDLINE, OVIDPsycINFO, CENTRAL, CINAHL, ClinicalTrials.gov, International Clinical Trials Registry, and Open Grey. We included 96 studies with a total of 4134 participants, of whom 762 (18.4) were healthy adults and 724 (17.5%) were adults with chronic conditions. Among adults with chronic conditions, ACT overestimated TST, compared to PSG [M = 22.42 min (CI 95%: 11.92, 32.91 min)] and SE [M = 5.21% (CI 95%: 1.41%-9.00%)]. ACT underestimated SOL [M = -7.70 min (CI 95%: -15.22, -0.18 min)], and WASO [M = -10.90 min (CI 95%: -26.01, 4.22 min)]. These differences were consistently larger between ACT and PSG sleep measures compared to healthy adults. Research is needed to better understand factors that influence the agreement between ACT and PSG among people with chronic conditions.
Project description:To determine the effect of light exposure on subsequent sleep characteristics under ambulatory field conditions.Twenty healthy participants were fitted with ambulatory polysomnography (PSG) and wrist-actigraphs to assess light exposure, rest-activity, sleep quality, timing, and architecture. Laboratory salivary dim-light melatonin onset was analyzed to determine endogenous circadian phase.Later circadian clock phase was associated with lower intensity (R2 = 0.34, ?2(1) = 7.19, p < .01), later light exposure (quadratic, controlling for daylength, R2 = 0.47, ?2(3) = 32.38, p < .0001), and to later sleep timing (R2 = 0.71, ?2(1) = 20.39, p < .0001). Those with later first exposure to more than 10 lux of light had more awakenings during subsequent sleep (controlled for daylength, R2 = 0.36, ?2(2) = 8.66, p < .05). Those with later light exposure subsequently had a shorter latency to first rapid eye movement (REM) sleep episode (R2 = 0.21, ?2(1) = 5.77, p < .05). Those with less light exposure subsequently had a higher percentage of REM sleep (R2 = 0.43, ?2(2) = 13.90, p < .001) in a clock phase modulated manner. Slow-wave sleep accumulation was observed to be larger after preceding exposure to high maximal intensity and early first light exposure (p < .05).The quality and architecture of sleep is associated with preceding light exposure. We propose that light exposure timing and intensity do not only modulate circadian-driven aspects of sleep but also homeostatic sleep pressure. These novel ambulatory PSG findings are the first to highlight the direct relationship between light and subsequent sleep, combining knowledge of homeostatic and circadian regulation of sleep by light. Upon confirmation by interventional studies, this hypothesis could change current understanding of sleep regulation and its relationship to prior light exposure.This study was not a clinical trial. The study was ethically approved and nationally registered (NL48468.042.14).
Project description:Study Objectives:To identify systematic biases across groups in objectively and subjectively measured sleep duration. Methods:We investigated concordance of self-reported habitual sleep duration compared with actigraphy- and single-night in-home polysomnography (PSG) across white, black, Hispanic, and Chinese participants in the Multi-Ethnic Study of Atherosclerosis. Results:Among 1910 adults, self-reported sleep duration, determined by differences between bed and wake times, was overestimated in all racial groups compared with PSG and actigraphy. Compared with whites (ρ = 0.45), correlations were significantly lower only in blacks (ρ = 0.28). Self-reporting bias for total sleep time compared with wrist actigraphy was 66 min (95% confidence interval [CI]: 61-71) for whites, 58 min (95% CI: 48-69) for blacks, 66 min (95% CI: 57-74) for Hispanics, and 60 min (95% CI: 49-70) for Chinese adults. Compared with PSG, self-reporting bias in whites at 73 min (95% CI: 67-79) was higher than in blacks (54 min [95% CI: 42-65]) and Chinese (49 min [95% CI: 37-61]) but not different from Hispanics (67 min [95% CI: 56-78]). Slight agreement/concordance was observed between self-reported and actigraphy-based total sleep time (kw = 0.14 for whites, 0.10 for blacks, 0.17 for Hispanics, and 0.11 for Chinese) and PSG (kw = 0.08 for whites, 0.04 for blacks, 0.05 for Hispanics, and 0.01 for Chinese) across race/ethnicity. Conclusions:Self-reported sleep duration overestimated objectively measured sleep across all races, and compared with PSG, overestimation is significantly greater in whites compared with blacks. Larger reporting bias reduces the ability to identify significant associations between sleep duration and health among blacks compared with whites. Sleep measurement property differences should be considered when comparing sleep indices across racial/ethnic groups.
Project description:To improve sleep quality assessment using a single chest-worn sensor by extracting body acceleration and sleep position changes.Sleep patterns of 21 participants (50.8 ± 12.8 years, 47.8% female) with self-reported sleep problems were simultaneously recorded using a chest sensor (Chest), an Actiwatch (Wrist), and polysomnography (PSG) during overnight sleep laboratory assessment. An algorithm for Chest was developed to detect sleep/wake epochs based on body acceleration and sleep position/postural changes data, which were then used to estimate sleep parameters of interest. Comparisons between Chest and Wrist with respect to PSG were performed. Identification of sleep/wake epochs was assessed by estimating sensitivity, specificity, and accuracy. Agreement between sensor-derived sleep parameters and PSG was assessed using correlation coefficients and Bland-Altman analysis.Chest identified sleep/wake epochs with an accuracy of on average 6% higher than Wrist (85.8% versus 79.8%). Similar trends were observed for sensitivity/specificity values. Correlation between Wrist and PSG was poor for most of the sleep parameters of interest (r = 0.0-0.3); however, Chest and PSG correlation showed moderate to strong agreement (r = 0.4-0.8) with relatively low bias and high precision bias (precision): 9.2 (13.2) minutes for sleep onset latency; 17.3(34.8) minutes for total sleep time; 7.5 (29.8) minutes for wake after sleep onset; and 2.0 (7.3)% for sleep efficacy.Combination of sleep postural/position changes and body acceleration improved detection of sleep/wake epochs compared to wrist acceleration alone. The chest sensors also improved estimation of sleep parameters of interest with stronger agreement with PSG. Our findings may expand the application of wearable sensors to clinically assess sleep outside of a sleep laboratory.
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.