Academia.edu no longer supports Internet Explorer.
To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser.
Cancer Cytopathology
…
2 pages
1 file
Journal of Sleep Research
Actigraphy (ACT) can enhance treatment for insomnia by providing objective estimates of sleep efficiency; however, only two studies have assessed the accuracy of actigraphy-based estimates of sleep efficiency (ACT-SE) in sleep-disordered samples studied at home. Both found poor correspondence with polysomnography-based estimates (PSG-SE). The current study tested that concordance in a third sample and piloted a method for improving ACT-SE. Participants in one of four diagnostic categories (panic disorder, post-traumatic stress disorder, comorbid post-traumatic stress and panic disorder and controls without sleep complaints) underwent in-home recording of sleep using concurrent ambulatory PSG and actigraphy. Precisely synchronized PSG and ACT recordings were obtained from 41 participants. Sleep efficiency was scored independently using conventional methods, and ACT-SE/PSG-SE concordance examined. Next, ACT data recorded initially at 0.5 Hz were resampled to 30-s epochs and rescaled on a perparticipant basis to yield optimized concordance between PSG-and ACTbased sleep efficiency estimates. Using standard scoring of ACT, the correlation between ACT-SE and PSG-SE across participants was statistically significant (r = 0.35, P < 0.025), although ACT-SE failed to replicate a main effect of diagnosis. Individualized calibration of ACT against a night of PSG yielded a significantly higher correlation between ACT-SE and PSG-SE (r = 0.65, P < 0.001; z = 1.692, P = 0.0452, one-tailed) and a significant main effect of diagnosis that was highly correspondent with the effect on PSG-SE. ACT-based estimates of sleep efficiency in sleep-disordered patients tested at home can be improved significantly by calibration against a single night of concurrent PSG.
Wrist actigraphy is commonly used to measure sleep, and hip actigraphy is commonly used to measure activity. It is unclear whether hip-based actigraphy can be used to measure sleep. This study assessed the validity of wrist actigraphy and hip actigraphy compared to polysomnography (PSG) for the measurement of sleep. 108 healthy young adults (22.7 ± 0.2 years) wore hip and wrist GTX3+ Actigraph during overnight PSG. Measurements of total sleep time (TST), sleep efficiency (SE), sleep onset latency (SOL) and wake after sleep onset (WASO) were derived and compared between wrist actigraphy, hip actigraphy and PSG. Sensitivity, specificity and accuracy of wrist actigraphy and hip actigraphy for each variable were derived from epoch-by-epoch comparison to PSG. Compared to PSG: TST and SE were similar by wrist actigraphy but overestimated by hip actigraphy (both by 14%); SOL was underestimated by wrist actigraphy and hip actigraphy (by 39 and 80%, respectively); WASO was overestimated by wrist actigraphy and underestimated by hip actigraphy (by 34 and 65%, respectively). Compared to PSG the sensitivity, specificity and accuracy of wrist actigraphy were 90, 46 and 84%, respectively; and of hip actigraphy were 99, 14 and 86%, respectively. This study showed that using existing algorithms, a GTX3+ Actigraph worn on the hip does not provide valid or accurate measures of sleep, mainly due to poor wake detection. Relative to the hip, a wrist worn GTX3+ Actigraph provided more valid measures of sleep, but with only moderate capability to detect periods of wake during the sleep period.
Health, 2013
Purpose: This study aimed to determine the feasibility and acceptability of actigraphy to monitor sleep quality and quantity in healthy self-rated good sleeper adults at home-based settings. Method: Sixteen healthy volunteers (age > 18) were invited to participate. Each participant was provided with a wrist actigraph device to be worn for 24-hour/day for seven consecutive days to monitor their sleep-wake patterns. Actigraphy data were downloaded using proprietary software to generate an individual sleep report. Participants also completed a set of self-reported Health Related Quality of Life (HRQOL) using WHO (five) Well Being Index (WBI) questionnaires. Results: Actigraphy was well accepted by all participants. Only 43.8% of the participants achieved normal total sleep time (TST) and 62.5% had a mean sleep efficiency value below the normal range. Despite a reduced quality of sleep among the participants, the self-reported HRQOL scores produced by the WHO-5 WBI showed a "fair" to "good" among the participants. Conclusions: To maintain healthy wellbeing, it is vital to have efficient and quality sleep. Insufficient and poor sleep may contribute to various health problems and hazardous outcomes. People often believe they have normal and efficient sleep, not realising they may be developing poor sleep habits. This study found that actigraphy can be easily utilized to monitor sleep-wake patterns at home-based settings. We proposed that actigraphy could be adapted for use in the primary care settings (e.g. community pharmacy) to improve the sleep health management in the community.
SLEEP, 2003
Sleepwake patterns are estimated from periods of activity and inactivity based on this movement. Since the publication of the previous practice parameter, 1 actigraph technology has markedly improved. In addition, actigraphy has been increasingly used to study patients with sleep disorders, to determine circadian rhythm activity cycles, and to determine the effect of a treatment on sleep. This update reports new evidence for the role of actigraphy in the study of sleep-wake patterns and circadian rhythms, published since the first expert review; grades the evidence available; and modifies and replaces the 1995 practice parameters.
Journal of Clinical Sleep Medicine, 2011
Study Objectives: Total sleep time (TST), sleep effi ciency (SE), sleep latency (SOL) and wake after sleep onset (WASO) assessed by actigraphy gathered in 3 different modes were compared to polysomnography (PSG) measurements to determine which mode corresponded highest to PSG. Associations of measurement error for TST (PSG-actigraphy) with demographics, medical history, exam data, and sleep characteristics were examined. Methods: Participants underwent in-home 12-channel PSG. Actigraphy data were collected in 3 modes: proportional integration mode (PIM), time above threshold (TAT) and zero crossings mode (ZCM). The analysis cohort was a subgroup of 889 men (mean age 76.4 years) from the MrOS Sleep Study with concurrently measured PSG and actigraphy. Intraclass correlation coeffi cients (ICCs) were used to compare the association between PSG and actigraphy. Results: The PIM mode of actigraphy corresponded moderately to PSG for all measures (ICCs 0.32 to 0.57), TAT a little lower (ICCs 0.17 to 0.47), and ZCM lower still (ICCs 0.16 to 0.33). The PIM mode corresponded best to PSG (ICCs TST 0.57; SE 0.46; SOL 0.23; WASO 0.54), though the estimations from PSG and PIM mode differed significantly (p < 0.01). The PIM mode overestimated TST by 13.2 min on average, but underestimated TST for those in certain subgroups: those with excessive daytime sleepiness, less sleep fragmentation, or more sleep disordered breathing (p < 0.05). Conclusions: Sleep parameters from the PIM and TAT modes of actigraphy corresponded reasonably well to PSG in this population, with the PIM mode correlating highest. Systematic measurement error was observed within subgroups with different sleep characteristics.
JOURNAL OF PAKISTAN MEDICAL ASSOCIATION, 2010
Behavioural and functional activity monitoring has a long history in sleep research. The term "Actigraphy" refers to methods using computerized wristwatch-size devices (generally placed on the wrist, but also on the ankle or trunk) to record the movement it undergoes. Collected data are displayed on a computer and analyzed for change in rhythm parameters that in turn provide an estimate on wake-sleep parameters (such as total sleep time, percent of time spent asleep, total wake time, percent of time spent awake and the number of awakenings). Actigraphy provides a useful, cost-effective, non-invasive and portable method for assessing specific sleep disorders. The present review is an amalgam of current knowledge with proposed clinical application and for research of actigraph. Actigraphy cannot stand alone as a diagnostic tool for all clinical groups. Particularly so with those diagnosed with sleep disorders with significant motility or long catatonic periods of wakefulness...
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021
Wearable actigraphy sensors have been useful tools for unobtrusive monitoring of sleep. The influence of the composition and characteristics of study groups such as normal sleep versus sleep disorders affecting the efficacy of sleep assessment using actigraphy has not been fully examined. In this study, we present multi-variate sleep models using actigraphy features obtained from wrist-worn sensors and evaluate the efficacy of sleep detection compared to the overnight polysomnography from two unique datasets: overnight actigraphy recordings in a control population of young healthy individuals (n=31) and 24-hour actigraphy recordings in a more heterogeneous population (n=27) comprised of normal and abnormal sleepers. We evaluate the performance of actigraphy derived logistic regression (LR) and random forest (RF) sleep models for both intra-dataset and inter-dataset training and cross-validation. Both the LR and RF sleep models for the healthy sleep dataset show an area under the receiver operating characteristic (AUROC) of 0.85±0.02 in the control sleep dataset among 50 random splits of training and testing evaluations. We find the AUROC performance from the heterogeneous sleep dataset involving sleep disorders to be relatively lower as 0.74±0.05 and 0.80±0.03 for LR and RF sleep models, respectively. Optimal sleep models trained using heterogeneous datasets perform very well when tested with the normal sleep dataset producing accuracy of ∼92%. Our study supports that using a more diverse training set benefits the sleep classifier model to be more generalizable for both healthy and abnormal sleepers.
Sleep advances, 2023
Comparisons of actigraphy findings between studies are challenging given differences between brand-specific algorithms. This issue may be minimized by using open-source algorithms. However, the accuracy of actigraphy-derived sleep parameters processed in open-source software needs to be assessed against polysomnography (PSG). Middle-aged adults from the Raine Study (n = 835; F 58%; Age 56.7 ± 5.6 years) completed one night of in-laboratory PSG and concurrent actigraphy (GT3X+ ActiGraph). Actigraphic measures of total sleep time (TST) were analyzed and processed using the opensource R-package GENEActiv and GENEA data in R (GGIR) with and without a sleep diary and additionally processed using proprietary software, ActiLife, for comparison. Bias and agreement (intraclass correlation coefficient) between actigraphy and PSG were examined. Common PSG and sleep health variables associated with the discrepancy between actigraphy, and PSG TST were examined using linear regression. Actigraphy, assessed in GGIR, with and without a sleep diary overestimated PSG TST by (mean ± SD) 31.0 ± 50.0 and 26.4 ± 69.0 minutes, respectively. This overestimation was greater (46.8 ± 50.4 minutes) when actigraphy was analyzed in ActiLife. Agreement between actigraphy and PSG TST was poor (ICC = 0.27-0.44) across all three methods of actigraphy analysis. Longer sleep onset latency and longer wakefulness after sleep onset were associated with overestimation of PSG TST. Open-source processing of actigraphy in a middle-aged community population, agreed poorly with PSG and, on average, overestimated TST. TST overestimation increased with increasing wakefulness overnight. Processing of actigraphy without a diary in GGIR was comparable to when a sleep diary was used and comparable to actigraphy processed with proprietary algorithms in ActiLife.
M-367. 2044 Sign 131. kāṇḍam காண்டம்² kāṇṭam, n. < kāṇḍa.‘water’ rebus: khaṇḍa 'implements’ + manda 'platform’ rebus: maṇḍā 'warehouse, workshop' (Konkani)Vikalpa. mēḍa 'platform, hillock' rebus meḍ 'iron’ + sal ‘splinter rebus: sal ‘workshop’. Thus, metal implements workshop, warehouse. dula ‘two’ rebus: dul ‘metal casting’ ayo, aya 'fish' rebus: aya 'iron' ayas 'metal alloy' (Rigveda) PLUS circumscript gaṇḍa 'four' rebus: khaṇḍa 'implements’ खांडा khāṇḍā A jag, notch, or indentation (as upon the edge of a tool or weapon). khaṇḍa 'implements’. Thus cast alloymetal implements, implements
Revista de Literatura (CSIC), 2011
Integrating Qualitative and Social Science Factors in Archaeological Modelling, 2019
arXiv (Cornell University), 2020
FOLD&R Fasti On Line Documents & Research, 387, 2017
Songklanakarin Journal of Science and Technology
Jurnal Inovasi Kesehatan Masyarakat, 2019
Arabian Journal of Geosciences, 2016
Journal of Hepatology, 2012
Quaternary Geochronology, 2017
Felsefix Uluslararası Felsefi Danışmanlık ve Etik Dergisi, 2024