Associations Between Preschoolers' Daytime and Nighttime Sleep Parameters
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This article examined associations between preschoolers' daytime and nighttime sleep parameters. A total of 63 preschoolers (65% boys; age: M = 4.15, SD = 0.62) participated. Sleep was assessed via actigraphy for 4 days and nights. Results are among the first to demonstrate significant associations between sleep parameters (especially sleep quality indexes) examined actigraphically at home and in child care contexts. Findings indicate that poor sleep quality indexed by greater sleep activity and awakenings, as well as less efficient sleep, were associated across nighttime sleep at home and daytime sleep in child care. Understanding connections between sleep across contexts has important implications for child care providers and parents as they attempt to facilitate child sleep during a developmental period of rapidly changing sleep patterns.Keywords:
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Abstract Introduction Sleep-wake state discrepancy is a common phenomenon identified among people with insomnia where greater sleep difficulties are self-reported in comparison with estimates obtained from objective assessment. This study provides the investigation into the sleep-wake state discrepancy and correlation between sleep diary (subjective) and actigraphy-derived (objective) sleep measures. Methods Participants included 136 cancer survivors with insomnia symptoms (M age = 63.8 ± 10.0; 55.9% female; 87.5% White) from baseline data in an ongoing clinical trial. Demographics, Insomnia Severity Index (ISI), 7-consecutive days of sleep diary and actigraphy data were obtained. Sleep measures included time in bed (TIB), total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), and sleep efficiency (SE%). Mean bias was defined as the discrepancy between sleep diary and actigraphy-derived sleep measures. The agreement between sleep diary and actigraphy-derived sleep measures were graphically assessed using the Bland-Altman plot. Using the mixed linear model approach, the estimated bias and 95% limits of agreement (LOA) were computed. Further, the Pearson correlation coefficient and concordance correlation coefficient (CCC), computed via maximum likelihood methods, were obtained. Results Self-reported TST and SE were shorter than derived by actigraphy (TST: 6.8 min. [95%CI: -18.7, 5.13]; and SE%: 0.7% [95%CI: -3.0, 2.0], respectively). Self-reported TIB, SOL, and WASO were longer than derived by actigraphy (TIB: 8.6 min. [95%CI: 3.7, 13.5]; SOL: 14.8 min. [95%CI: 9.4, 20.2]; and WASO: 20.7 min. [95%CI: 9.4, 20.2], respectively). Moderate to high agreement and correlation were found between the sleep diary and actigraphy-derived TIB (CCC=0.78; r=0.73) and TST (CCC=0.58; r=0.51). In contrast, SOL (CCC=0.48; r=0.35), WASO (CCC=0.36; r=0.18), and SE% (CCC=0.39; r=0.22) showed only fair or poor agreement and correlation. Calculated Bland-Altman LOA between sleep diary and actigraphy derived measures were as follows: TIB (95%LOA: -121.5, 138.7), TST (95%LOA: -197.9, 184.3), SOL (95%LOA: -82.5, 112.1), WASO (95%LOA: -123.5, 164.8), and SE% (95%LOA: -0.37, 0.36). Conclusion Among a heterogeneous sample of cancer survivors with insomnia symptoms, average self-reported sleep duration and efficiency were shorter and self-reported TIB, SOL, and WASO were longer than objectively measured sleep measures. Agreement between two methods varied across different measures. Support (if any) NIH/NINR R01NR018215 (Dean), ClinicalTrials-NCT03810365
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ABSTRACT OBJECTIVE: Differences in sleep results due to the placement of actigraphy devices (non-dominant vs. dominant wrist) are yet to be determined. METHODS: 65 nights of data from 13 adult participants was collected while participants wore two actigraphy devices, one on each wrist. Sleep indices including total sleep time (TST), total time in bed (TTB), sleep efficiency (SE%), sleep latency (SL), wake after sleep onset (WASO), sleep onset time (SOT) and wake time (WT) were assessed between the two devices. RESULTS: There were no significant differences between devices for any of the measured sleep variables (p>0.05). SE%, SL and WASO resulted in high correlations between devices (0.89, 0.89 and 0.76, respectively), with all other sleep variables resulting in very high correlations (>0.90) between devices. CONCLUSIONS: Based on our results, it does not seem critical which wrist the actigraphy device is worn on for measuring key sleep variables.
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Abstract Introduction Non-contact devices (NCDs) have been developed to measure sleep longitudinally and unobtrusively in the naturalistic home setting. We compared longitudinal measurements from a wrist actigraph (Actiwatch-2, Philips Respironics) and from a NCD (SleepScore Max, SleepScore Labs) in a sample of adults with insomnia Methods N=71 adults (ages 39.0±13.0y; 50 women) who met ICSD-3 criteria for chronic insomnia and were otherwise healthy participated in an at-home sleep monitoring study. Participants continuously wore the actigraph for one week, then used the NCD to record only nightly sleep periods for the next 8 weeks. Week-by-week within-subject averages and standard deviations (SDs) over days were assessed for five major sleep parameters: total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), time in bed (TIB), and sleep efficiency (SE). These sleep parameters were analyzed with mixed-effects ANOVA comparing week one (actigraphy) to the next 8 weeks (NCD), and correlations between the first week (actigraphy) and second week (NCD) were calculated. Results Significant differences for actigraphy versus NCD were found for the weekly averages of SOL and WASO (F>25.8, p<0.001). The NCD measured longer average SOL (M±SEM=25.0±1.5min) than actigraphy (12.6±2.0min) and less average WASO (40.5±2.7min) than actigraphy (51.1±3.2min). Further, significant differences were found for the weekly within-subject SDs of TST and WASO (F>7.52, p<0.01). The NCD measured greater SD for TST (71.6±2.8min) than actigraphy (60.0±4.7min) and greater SD for WASO (25.0±1.4min) than actigraphy (19.2±2.2min). Actigraphy and NCD weekly averages were positively correlated for TST, WASO, TIB, and SE (p<0.001), but not SOL (r=0.03, p=0.80). Similarly, weekly within-subject SDs were positively correlated for TST, WASO, TIB, and SE (p≤0.05), but not SOL (r=–0.03, p=0.81). Conclusion Actigraphy and NCD were not used simultaneously, precluding a direct comparison between these measurement modalities. Nonetheless, in this ecologically valid context, significant differences were only found for the weekly averages of SOL and WASO and for the weekly within-subject variability of SOL and TST, with significant correlations between the devices for all variables except SOL. Although actigraphy tends to underestimate SOL, NCD validation against polysomnography in chronic insomnia is warranted. Support (If Any) NIH grant KL2TR002317; research devices provided by SleepScore Labs.
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This analysis examined the discrepancy between sleep diary and actigraphy measurements in breast cancer survivors (BCS) with insomnia. BCS from communities in Western U.S. provided demographic/medical information, insomnia, mood, and fatigue data at baseline. Averaged over 5 weeks, actigraphy measured 55.75 minutes (SD = 112.42) less total sleep time (TST), and 85.19 minutes (SD = 81.36) more wake after sleep onset (WASO) than diaries. Some women showed agreement between measures; others were more variable. There were no significant relationships between TST and WASO discrepancy and participant characteristics. There may be sleep differences in BCS that results in greater perceived TST and less WASO reported in diaries. Measurements discrepancy is a significant concern needing further evaluation of medical populations with insomnia.
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Purpose: Actigraphy-based sleep detection algorithms were mostly validated using nighttime sleep, and their performance in detecting daytime sleep is unclear. We evaluated and compared the performance of Actiware and the Cole-Kripke algorithm (C-K) – two commonly used actigraphy-based algorithms – in detecting daytime and nighttime sleep. Participants and Methods: Twenty-five healthy young adults were monitored by polysomnography and actigraphy during two in-lab protocols with scheduled nighttime and/or daytime sleep (within-subject design). Mixed-effect models were conducted to compare the sensitivity, specificity, and F1 score (a less-biased measure of accuracy) of Actiware (with low/medium/high threshold setting, separately) and C-K in detecting sleep epochs from actigraphy recordings during nighttime/daytime. t -tests and intraclass correlation coefficients were used to assess the agreement between actigraphy-based algorithms and polysomnography in scoring total sleep time (TST). Results: Sensitivity was similar between nighttime (Actiware: 0.93– 0.99 across threshold settings; C-K: 0.61) and daytime sleep (Actiware: 0.93– 0.99; C-K: 0.66) for both the C-K and Actiware (daytime/nighttime×algorithm interaction: p > 0.1). Specificity for daytime sleep was lower (Actiware: 0.35– 0.54; C-K: 0.91) than that for nighttime sleep (Actiware: 0.37– 0.62; C-K: 0.93; p = 0.001). Specificity was also higher for C-K than Actiware (p < 0.001), with no daytime/nighttime×algorithm interaction (p > 0.1). C-K had lower F1 (nighttime = 0.74; daytime = 0.77) than Actiware (nighttime = 0.95– 0.98; daytime = 0.90– 0.91) for both nighttime and daytime sleep (all p < 0.05). The daytime-nighttime difference in F1 was opposite for Actiware (daytime: 0.90– 0.91; nighttime: 0.95– 0.98) and C-K (daytime: 0.77; nighttime: 0.74; interaction p = 0.003). Bias in TST was lowest in Actiware (with medium-threshold) for nighttime sleep (underestimation of 5.99 min/8h) and in Actiware (with low-threshold) for daytime sleep (overestimation of 17.75 min/8h). Conclusion: Daytime/nighttime sleep affected specificity and F1 but not sensitivity of actigraphy-based sleep scoring. Overall, Actiware performed better than the C-K algorithm. Actiware with medium-threshold was the least biased in estimating nighttime TST, and Actiware with low-threshold was the least biased in estimating daytime TST. Keywords: Actiware, Cole-Kripke algorithm, sleep scoring, shift worker, circadian rhythms
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Objective/Background: Actigraphy is an inexpensive and objective wrist-worn activity sensor that has been validated for the measurement of sleep onset latency (SOL), number of awakenings (NWAK), wake after sleep onset (WASO), total sleep time (TST), and sleep efficiency (SE) in both middle-aged and older adults with insomnia. However, actigraphy has not been evaluated in young adults. In addition, most previous studies compared actigraphy to in-lab polysomnography (PSG), but none have compared actigraphy to more ecologically valid ambulatory polysomnography.Participants: 21 young adults (mean age = 19.90 ± 2.19 years; n = 13 women) determined to have chronic primary insomnia through structured clinical interviews.Methods: Sleep diaries, actigraphy, and ambulatory PSG data were obtained over a single night to obtain measures of SOL, NWAK, WASO, time spent in bed after final awakening in the morning (TWAK), TST, and SE.Results: Actigraphy was a valid estimate of SOL, WASO, TST, and SE, based on significant correlations (r = 0.45 to 0.87), nonsignificant mean differences between actigraphy and PSG, and inspection of actigraphy bias from Bland Altman plots (SOL α = 1.52, WASO α = 7.95, TST α = −8.60, SE α = −1.38).Conclusions: Actigraphy was a valid objective measure of SOL, WASO, TST, and SE in a young adult insomnia sample, as compared to ambulatory PSG. Actigraphy may be a valid alternative for assessing sleep in young adults with insomnia when more costly PSG measures are not feasible.
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We validated actigraphy for detecting sleep and wakefulness versus polysomnography (PSG).
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Background: Quality of life in patients with heart failure (HF) can be significantly impacted by poor sleep and its daytime consequences. As more attention is being paid to the sleep problems of HF patients, it is important to evaluate the degree of congruence between subjective and objective sleep measurements in this patient group. Purpose: This study was developed to evaluate the congruence between sleep parameters as measured using a wrist-worn ActiGraph and a daily sleep log in patients with stable HF. Methods: Forty-three HF patients aged 40-92 years served as subjects. Sleep parameters were derived from actigraphy and a daily sleep log by averaging scores for 7 nights. Results: There were significant differences in wake time after sleep onset (WASO) and total sleep time between the sleep log and the ActiGraph (both ps < .001). Neither WASO nor sleep onset latency, both derived from the sleep log, correlated significantly with actigraphy variables. The mean bias for WASO and total sleep time between methods was 54.1 min (SD = 47.5 min) and 109.3 min (SD = 91.68 min) as assessed using a Bland-Altman analysis. A majority (83.7%) of participants experienced sleep disturbances as assessed by actigraphy. However, fewer (53.5%) had sleep disturbances as assessed using the sleep log. Conclusion: A considerable degree of incongruence between actigraphy- and sleep log-derived measures of sleep exists in patients with stable HF.
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To assess the usefulness of actigraphy for assessment of nighttime sleep measures in patients with Parkinson's disease (PD). Participants underwent overnight sleep assessment simultaneously by polysomnography (PSG) and actigraphy. Overnight sleep study in academic sleep research laboratory. Sixty-one patients (mean age 67.74 ± 8.88 y) with mild to moderate PD. Sleep measures including total sleep time (TST), sleep efficiency (SE), wake after sleep onset (WASO), and sleep onset latency (SOL) were calculated independently from data derived from PSG and from actigraphy. Different actigraphy scoring settings were compared. No single tested actigraphy scoring setting was optimal for all sleep measures. A customized setting of an activity threshold of 10, with five consecutive immobile minutes for sleep onset, yielded the combination of mean TST, SE, and WASO values that best approximated mean values determined by PSG with differences of 6.05 ± 85.67 min for TST, 1.1 ± 0.641% for SE, and 4.35 ± 59.56 min for WASO. There were significant but moderate correlations between actigraphy and PSG measurements (rs = 0.496, P < 0.001 for TST, rs = 0.384, P = 0.002 for SE, and rs = 0.400, P = 0.001 for WASO) using these settings. Greater disease stage was associated with greater differences between TST (R2 = 0.099, beta = 0.315, P = 0.018), SE (R2 = 0.107, beta = 0.327, P = 0.014), and WASO (R2 = 0.094, beta = 0.307, P = 0.021) values derived by actigraphy and PSG explaining some of the variability. Using a setting of 10 immobile min for sleep onset yielded a mean SOL that was within 1 min of that estimated by PSG. However SOL values determined by actigraphy and PSG were not significantly correlated at any tested setting. Our results suggest that actigraphy may be useful for measurement of mean TST, SE, and WASO values in groups of patients with mild to moderate Parkinson's disease. However, there is a significant degree of variability in accuracy among individual patients. The importance of determining optimal scoring parameters for each population studied is underscored.
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Background The sleep and circadian rhythm patterns associated with smartphone use, which are influenced by mental activities, might be closely linked to sleep quality and depressive symptoms, similar to the conventional actigraphy-based assessments of physical activity. Objective The primary objective of this study was to develop app-defined circadian rhythm and sleep indicators and compare them with actigraphy-derived measures. Additionally, we aimed to explore the clinical correlations of these indicators in individuals with insomnia and healthy controls. Methods The mobile app “Rhythm” was developed to record smartphone use time stamps and calculate circadian rhythms in 33 patients with insomnia and 33 age- and gender-matched healthy controls, totaling 2097 person-days. Simultaneously, we used standard actigraphy to quantify participants’ sleep-wake cycles. Sleep indicators included sleep onset, wake time (WT), wake after sleep onset (WASO), and the number of awakenings (NAWK). Circadian rhythm metrics quantified the relative amplitude, interdaily stability, and intradaily variability based on either smartphone use or physical activity data. Results Comparisons between app-defined and actigraphy-defined sleep onsets, WTs, total sleep times, and NAWK did not reveal any significant differences (all P>.05). Both app-defined and actigraphy-defined sleep indicators successfully captured clinical features of insomnia, indicating prolonged WASO, increased NAWK, and delayed sleep onset and WT in patients with insomnia compared with healthy controls. The Pittsburgh Sleep Quality Index scores were positively correlated with WASO and NAWK, regardless of whether they were measured by the app or actigraphy. Depressive symptom scores were positively correlated with app-defined intradaily variability (β=9.786, SD 3.756; P=.01) and negatively correlated with actigraphy-based relative amplitude (β=–21.693, SD 8.214; P=.01), indicating disrupted circadian rhythmicity in individuals with depression. However, depressive symptom scores were negatively correlated with actigraphy-based intradaily variability (β=–7.877, SD 3.110; P=.01) and not significantly correlated with app-defined relative amplitude (β=–3.859, SD 12.352; P=.76). Conclusions This study highlights the potential of smartphone-derived sleep and circadian rhythms as digital biomarkers, complementing standard actigraphy indicators. Although significant correlations with clinical manifestations of insomnia were observed, limitations in the evidence and the need for further research on predictive utility should be considered. Nonetheless, smartphone data hold promise for enhancing sleep monitoring and mental health assessments in digital health research.
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