Cited by 298Sleep and related disordersCircadian rhythm and melatoninSleep and Work-Related Fatigue

The DOI matches its Crossref record; 21 of the 22 references it cites were found in a registry or an index.

Actigraphy-based sleep estimation in adolescents and adults: a comparison with polysomnography using two scoring algorithms

Open access

Quante, Mirja, Kaplan, Emily R, Cailler, Michael, Rueschman, Michael, Wang, Rui, Weng, Jia, Taveras, Elsie M, Redline, Susan·2018·Nature and Science of Sleep

DOI 10.2147/nss.s151085PMC PMC5779275

Open this paper in CoChat — free account Open PDF

Abstract

Objectives: Actigraphy is widely used to estimate sleep–wake time, despite limited information regarding the comparability of different devices and algorithms. We compared estimates of sleep–wake times determined by two wrist actigraphs (GT3X+ versus Actiwatch Spectrum [AWS]) to in-home polysomnography (PSG), using two algorithms (Sadeh and Cole–Kripke) for the GT3X+ recordings. Subjects and methods: Participants included a sample of 35 healthy volunteers (13 school children and 22 adults, 46% male) from Boston, MA, USA. Twenty-two adults wore the GT3X+ and AWS simultaneously for at least five consecutive days and nights. In addition, actigraphy and PSG were concurrently measured in 12 of these adults and another 13 children over a single night. We used intraclass correlation coefficients (ICCs), epoch-by-epoch comparisons, paired t -tests, and Bland–Altman plots to determine the level of agreement between actigraphy and PSG, and differences between devices and algorithms. Results: Each actigraph showed comparable accuracy (0.81–0.86) for sleep–wake estimation compared to PSG. When analyzing data from the GT3X+, the Cole–Kripke algorithm was more sensitive (0.88–0.96) to detect sleep, but less specific (0.35–0.64) to detect wake than the Sadeh algorithm (sensitivity: 0.82–0.91, specificity: 0.47–0.68). Total sleep time measured using the GT3X+ with both algorithms was similar to that obtained by PSG (ICC=0.64–0.88). In contrast, agreement between the GT3X+ and PSG wake after sleep onset was poor (ICC=0.00–0.10). In adults, the GT3X+ using the Cole–Kripke algorithm provided data comparable to the AWS (mean bias=3.7±19.7 minutes for total sleep time and 8.0±14.2 minutes for wake after sleep onset). Conclusion: The two actigraphs provided comparable and accurate data compared to PSG, although both poorly identified wake episodes (i.e., had low specificity). Use of actigraphy scoring algorithm influenced the mean bias and level of agreement in sleep–wake times estimates. The GT3X+, when analyzed by the Cole–Kripke, but not the Sadeh algorithm, provided comparable data to the AWS. Keywords: validation, actigraphy, polysomnography, scoring algorithm

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.2147/nss.s151085

Cites

21 of 21 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Untitled work
(2007) · Cited by 6,530
The consensus sleep diary: standardizing prospective sleep self-monitoring
Carney, C. E., Buysse, D. J., Ancoli-Israel, S., Edinger, J. D., Krystal, A. D., Lichstein, K. L., & Morin, C. M. (2012) · Sleep · doi:10.5665/sleep.1642 · Cited by 2,172 · cited 1×
“, San Francisco, CA, USA).(13) All logs and participants’ answers were sent to a secure webbased dashboard.”
Automatic sleep/wake identification from wrist activity.
Cole, R., Kripke, D., Gruen, W., Mullaney, D., & Gillin, J. (1992) · Sleep · doi:10.1093/sleep/15.5.461 · Cited by 1,971
Activity-Based Sleep-Wake Identification: An Empirical Test of Methodological Issues
Sadeh, A., Sharkey, M., & Carskadon, M. A. (1994) · Sleep · doi:10.1093/sleep/17.3.201 · Cited by 1,487
The role and validity of actigraphy in sleep medicine: an update
Sadeh, A. (2011) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2010.10.001 · Cited by 1,415
Use of actigraphy for assessment in pediatric sleep research
Meltzer, L. J., Montgomery‐Downs, H. E., Insana, S., & Walsh, C. M. (2012) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2011.10.002 · Cited by 679
Further Validation of Actigraphy for Sleep Studies
de Souza, L., Benedito‐Silva, A. A., Pires, M. L. N., Poyares, D. R., Tufik, S., & Calil, H. M. (2003) · SLEEP · doi:10.1093/sleep/26.1.81 · Cited by 587
Direct Comparison of Two New Actigraphs and Polysomnography in Children and Adolescents
Meltzer, L. J., Walsh, C. M., Traylor, J. T., & Westin, A. M. L. (2011) · SLEEP · doi:10.5665/sleep.1608 · Cited by 339
Agreement of different methods for assessing sleep characteristics: a comparison of two actigraphs, wrist and hip placement, and self-report with polysomnography
Zinkhan, M., Berger, K., Hense, S., Nagel, M., Obst, A., Koch, B., Penzel, T., Fietze, I., Ahrens, W., Young, P., et al. (2014) · Sleep Medicine · doi:10.1016/j.sleep.2014.04.015 · Cited by 232
A Novel Adaptive Wrist Actigraphy Algorithm for Sleep-Wake Assessment in Sleep Apnea Patients
Hedner, J. A., Pillar, G., Pittman, S. D., Zou, D., Grote, L., & White, D. P. (2004) · SLEEP · doi:10.1093/sleep/27.8.1560 · Cited by 214

Flagged first, then by citations

Show all 22 references

1 of the 22 references couldn’t be matched to a known paper.

Sorted by citations

Wearable Sleep Technology in Clinical and Research Settings
DE ZAMBOTTI, M., CELLINI, N., GOLDSTONE, A., COLRAIN, I. M., & BAKER, F. C. (2019) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001947 · Cited by 564
Review of Wearable Devices and Data Collection Considerations for Connected Health
Vijayan, V., Connolly, J. P., Condell, J., McKelvey, N., & Gardiner, P. (2021) · Sensors · doi:10.3390/s21165589 · Cited by 435
Sleep disturbances and sleep disorders in adults living with chronic pain: a meta-analysis
Mathias, J. L., Cant, M. L., & Burke, A. L. J. (2018) · Sleep Medicine · doi:10.1016/j.sleep.2018.05.023 · Cited by 310
Mobile EEG in research on neurodevelopmental disorders: Opportunities and challenges
Lau‐Zhu, A., Lau, M., & McLoughlin, G. (2019) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2019.100635 · Cited by 220
Chronotype, Social Jet Lag, and Cardiometabolic Risk Factors in Early Adolescence
Feliciano, E. M. C., Rifas‐Shiman, S. L., Quante, M., Redline, S. S., Oken, E., & Taveras, E. M. (2019) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2019.3089 · Cited by 203
Self-citation
A Systematic Review of Sensing Technologies for Wearable Sleep Staging
Imtiaz, S. A. (2021) · Sensors · doi:10.3390/s21051562 · Cited by 174
Bi-directional relations between stress and self-reported and actigraphy-assessed sleep: a daily intensive longitudinal study
Yap, Y., Slavish, D. C., Taylor, D. J., Bei, B., & Wiley, J. F. (2019) · SLEEP · doi:10.1093/sleep/zsz250 · Cited by 143
The Impact of Optimized Daylight and Views on the Sleep Duration and Cognitive Performance of Office Workers
Boubekri, M., Lee, J., MacNaughton, P., Woo, M. K., Schuyler, L., Tinianov, B. D., & Satish, U. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17093219 · Cited by 120
Comparing polysomnography, actigraphy, and sleep diary in the home environment: The Study of Women’s Health Across the Nation (SWAN) Sleep Study
Lehrer, H. M., Yao, Z., Krafty, R. T., Evans, M. A., Buysse, D. J., Kravitz, H. M., Matthews, K. A., Gold, E. B., Harlow, S. D., Samuelsson, L. B., et al. (2022) · SLEEP Advances · doi:10.1093/sleepadvances/zpac001 · Cited by 132
Past, Present, and Future of Multisensory Wearable Technology to Monitor Sleep and Circadian Rhythms
Lujan, M. R., Perez-Pozuelo, I., & Grandner, M. A. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.721919 · Cited by 115
Multi-Night Validation of a Sleep Tracking Ring in Adolescents Compared with a Research Actigraph and Polysomnography
Chee, N. I. Y. N., Ghorbani, S., Golkashani, H. A., Leong, R. L. F., Ong, J. L., & Chee, M. W. L. (2021) · Nature and Science of Sleep · doi:10.2147/nss.s286070 · Cited by 108
Home monitoring of sleep with a temporary-tattoo EEG, EOG and EMG electrode array: a feasibility study
Shustak, S., Inzelberg, L., Steinberg, S., Rand, D., David‐Pur, M., Hillel, I., Katzav, S., Fahoum, F., Vos, M. D., Mirelman, A., et al. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aafa05 · Cited by 103
Automating sleep stage classification using wireless, wearable sensors
Boe, A. J., McGee Koch, L. L., O’Brien, M. K., Shawen, N., Rogers, J. A., Lieber, R. L., Reid, K. J., Zee, P. C., & Jayaraman, A. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0210-1 · Cited by 98
ActiGraph GT3X+ and Actical Wrist and Hip Worn Accelerometers for Sleep and Wake Indices in Young Children Using an Automated Algorithm: Validation With Polysomnography
Smith, C. P., Galland, B. C., Taylor, R. W., & Meredith-Jones, K. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00958 · Cited by 93
Textile triboelectric nanogenerators for self-powered biomonitoring
Lama, J. E., Yau, A., Chen, G., Sivakumar, A., Zhao, X., & Chen, J. (2021) · Journal of Materials Chemistry A · doi:10.1039/d1ta02518j · Cited by 90
Sleep influences daily suicidal ideation through affective reactivity to interpersonal events among high‐risk adolescents and young adults
Hamilton, J. L., Tsypes, A., Zelazny, J., Sewall, C. J. R., Rode, N., Merranko, J., Brent, D. A., Goldstein, T. R., & Franzen, P. L. (2023) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13651 · Cited by 71
Zeitgebers and their association with rest-activity patterns
Quante, M., Mariani, S., Weng, J. F., Marinac, C. R., Kaplan, E. R., Rueschman, M., Mitchell, J. A., James, P., Hipp, J. A., Feliciano, E. M. C., et al. (2018) · Chronobiology International · doi:10.1080/07420528.2018.1527347 · Cited by 70
Self-citation
Wearables in Chronomedicine and Interpretation of Circadian Health
Gubin, D. G., Weinert, D., Stefani, O., Otsuka, K., Borisenkov, M. F., & Cornélissen, G. (2025) · Diagnostics · doi:10.3390/diagnostics15030327 · Cited by 75
Validity of Actigraphy Compared to Polysomnography for Sleep Assessment in Children With Autism Spectrum Disorder
Yavuz-Kodat, E., Reynaud, È., Geoffray, M., Limousin, N., Franco, P., Bourgin, P., & Schroder, C. M. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00551 · Cited by 63
Selecting a sleep tracker from EEG-based, iteratively improved, low-cost multisensor, and actigraphy-only devices
Ong, J. L., Golkashani, H. A., Ghorbani, S., Wong, K. F., Chee, N. I. Y. N., Willoughby, A. R., & Chee, M. W. L. (2023) · Sleep Health · doi:10.1016/j.sleh.2023.11.005 · Cited by 69
Non-pharmacological interventions for sleep disturbances in people with dementia
Wilfling, D., Calo, S., Dichter, M. N., Meyer, G., Möhler, R., & Köpke, S. (2023) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd011881.pub2 · Cited by 59
Sleep assessment devices: types, market analysis, and a critical view on accuracy and validation
Valle, V. I.-D., Silva, J., Navarro-Illana, E., & Cauli, O. (2019) · Expert Review of Medical Devices · doi:10.1080/17434440.2019.1693890 · Cited by 56
Broken Windows, Broken Zzs: Poor Housing and Neighborhood Conditions Are Associated with Objective Measures of Sleep Health
Troxel, W., Haas, A. C., Ghosh‐Dastidar, B., Holliday, S. B., Richardson, A. S., Schwartz, H. L., Gary‐Webb, T. L., Hale, L. E., Buysse, D. J., Buman, M. P., et al. (2020) · Journal of Urban Health · doi:10.1007/s11524-019-00418-5 · Cited by 57
Food Insecurity is Associated with Objectively Measured Sleep Problems
Troxel, W., Haas, A. C., Ghosh‐Dastidar, B., Richardson, A. S., Hale, L. E., Buysse, D. J., Buman, M. P., Kurka, J. M., & Dubowitz, T. (2019) · Behavioral Sleep Medicine · doi:10.1080/15402002.2019.1669605 · Cited by 56
An algorithm for actigraphy-based sleep/wake scoring: Comparison with polysomnography
Lüdtke, S., Hermann, W., Kirste, T., Beneš, H., & Teipel, S. J. (2020) · Clinical Neurophysiology · doi:10.1016/j.clinph.2020.10.019 · Cited by 55
Validation of a Device for the Ambulatory Monitoring of Sleep Patterns: A Pilot Study on Parkinson's Disease
Madrid-Navarro, C. J., Puertas, F. J., Sevilla, F. E., Campos, M., Abellán, F. R., Rol, M. A., & Madrid, J. A. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00356 · Cited by 55
Relationships between objectives sleep parameters and brain amyloid load in subjects at risk for Alzheimer’s disease: the INSIGHT-preAD Study
Ettore, É., Bakardjian, H., Sole, M., Lévy, M., Habert, M., Gabelle, A., Dubois, B., Robert, P. H., & David, R. (2019) · SLEEP · doi:10.1093/sleep/zsz137 · Cited by 55
Personalized Sleep Parameters Estimation from Actigraphy: A Machine Learning Approach
Khademi, A., EL‐Manzalawy, Y., Master, L., Buxton, O. M., & Honavar, V. (2019) · Nature and Science of Sleep · doi:10.2147/nss.s220716 · Cited by 54
Associations between brain structure and sleep patterns across adolescent development
Jalbrzikowski, M., Hayes, R. A., Scully, K. E., Franzen, P. L., Hasler, B. P., Siegle, G. J., Buysse, D. J., Dahl, R. E., Forbes, E. E., Ladouceur, C. D., et al. (2021) · SLEEP · doi:10.1093/sleep/zsab120 · Cited by 51
Validation of an automated sleep detection algorithm using data from multiple accelerometer brands
Plekhanova, T., Rowlands, A. V., Davies, M. J., Hall, A. P., Yates, T. E., & Edwardson, C. L. (2022) · Journal of Sleep Research · doi:10.1111/jsr.13760 · Cited by 49
Quality Evaluation of Free-living Validation Studies for the Assessment of 24-Hour Physical Behavior in Adults via Wearables: Systematic Review
Giurgiu, M., Timm, I., Becker, M. L., Schmidt, S., Wunsch, K., Nissen, R., Davidovski, D., Bussmann, J. B. J., Nigg, C. R., Reichert, M., et al. (2022) · JMIR mhealth and uhealth · doi:10.2196/36377 · Cited by 48
Light enhanced cognitive behavioral therapy for insomnia and fatigue during chemotherapy for breast cancer: a randomized controlled trial
Bean, H. R., Diggens, J., Ftanou, M., Alexander, M., Stafford, L., Bei, B., Francis, P. A., & Wiley, J. F. (2021) · SLEEP · doi:10.1093/sleep/zsab246 · Cited by 47
Sleep and circadian disruption in bipolar disorders: From psychopathology to digital phenotyping in clinical practice
Tonon, A. C., Nexha, A., da Silva, M. M., Gomes, F. A., Hidalgo, M. P. L., & Frey, B. N. (2024) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.13729 · Cited by 51
Stellate ganglion block alleviates postoperative sleep disturbance in patients undergoing radical surgery for gastrointestinal malignancies
Yan, S., Wang, Y., Yu, L., Xia, W., Xue, F. W., Yu, Y., Yuan, B., Li, N., Li, H., Liang, H., et al. (2023) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.10632 · Cited by 40
Impact of COVID-19 Pandemic on School-Aged Children’s Physical Activity, Screen Time, and Sleep in Hong Kong: A Cross-Sectional Repeated Measures Study
So, H., Chua, G. T., Yip, K. M., Tung, K. T. S., Wong, R. S. M., Louie, L. H.-T., Tso, W. W. Y., Wong, I. C. K., Yam, J. C. S., Kwan, M. Y. W., et al. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph191710539 · Cited by 36
Assessment of 24-hour physical behaviour in children and adolescents via wearables: a systematic review of free-living validation studies
Giurgiu, M., Kolb, S., Nigg, C., Burchartz, A., Timm, I., Becker, M. L., Rulf, E., Doster, A.-K., Koch, E. D., Bussmann, J. B. J., et al. (2022) · BMJ Open Sport & Exercise Medicine · doi:10.1136/bmjsem-2021-001267 · Cited by 39
Association of sleep with cognition and beta amyloid accumulation in adults with Down syndrome
Cody, K. A., Piro‐Gambetti, B., Zammit, M. D., Christian, B. T., Handen, B. L., Klunk, W. E., Zaman, S. H., Johnson, S. C., Plante, D. T., & Hartley, S. L. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.04.018 · Cited by 38
Replacing school and out-of-school sedentary behaviors with physical activity and its associations with adiposity in children and adolescents: a compositional isotemporal substitution analysis
Gába, A., Dygrýn, J., Štefelová, N., Rubín, L., Hron, K., & Jakubec, L. (2021) · Environmental Health and Preventive Medicine · doi:10.1186/s12199-021-00932-6 · Cited by 38
Field-based Measurement of Sleep: Agreement between Six Commercial Activity Monitors and a Validated Accelerometer
Kubala, A. G., Gibbs, B. B., Buysse, D. J., Patel, S. R., Hall, M. H., & Kline, C. E. (2019) · Behavioral Sleep Medicine · doi:10.1080/15402002.2019.1651316 · Cited by 33
Intermittent Fasting and Sleep: A Review of Human Trials
McStay, M., Gabel, K., Cienfuegos, S., Ezpeleta, M., Lin, S., & Varady, K. A. (2021) · Nutrients · doi:10.3390/nu13103489 · Cited by 40
Sleep Characteristics and Measures of Glucose Metabolism in Blacks: The Jackson Heart Study
Yano, Y., Gao, Y. L., Johnson, D. A., Carnethon, M. R., Correa, A., Mittleman, M. A., Sims, M., Mostofsky, E., Wilson, J. G., & Redline, S. S. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.119.013209 · Cited by 33
Self-citation
Validity of chronotype questionnaires in adolescents: Correlations with actigraphy
Paciello, L. M., Quante, M., Weidenauer, C., Rueschman, M., Nieratschker, V., Poets, C. F., & Randler, C. (2022) · Journal of Sleep Research · doi:10.1111/jsr.13576 · Cited by 32
Self-citation
Validation of actigraphy sleep metrics in children aged 8 to 16 years: considerations for device type, placement and algorithms.
Meredith-Jones, K. A., Haszard, J. J., Graham-DeMello, A., Campbell, A., Stewart, T., Galland, B. C., Cox, A., Kennedy, G., Duncan, S., & Taylor, R. W. (2024) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-024-01590-x · Cited by 32
Sleep monitoring with the Apple Watch: comparison to a clinically validated actigraph
Roomkham, S., Hittle, M., Cheung, J., Lovell, D. R., Mignot, E. J.-M., & Perrin, D. (2019) · F1000Research · doi:10.12688/f1000research.19020.1 · Cited by 31
Treating subclinical and clinical symptoms of insomnia with a mindfulness-based smartphone application: a pilot study
Low, T., Conduit, R., Varma, P., Meaklim, H., & Jackson, M. L. (2020) · Internet Interventions · doi:10.1016/j.invent.2020.100335 · Cited by 30
“, 2003) and successfully validated against polysomnography (PSG) for insomnia (Lichstein et al., 2006; Quante et al., 2018).”
BackgroundMethod
The effects of sleep deprivation, acute hypoxia, and exercise on cognitive performance: A multi-experiment combined stressors study
Williams, T. B., Badariotti, J. I., Corbett, J., Dicks, M., Neupert, E. C., McMorris, T., Ando, S., Parker, M. O., Thelwell, R., Causer, A. J., et al. (2023) · Physiology & Behavior · doi:10.1016/j.physbeh.2023.114409 · Cited by 33
Sleep Measurement Using Wrist-Worn Accelerometer Data Compared with Polysomnography
Chase, J. D., Busa, M. A., STAUDENMAYER, J. W., & Sirard, J. R. (2022) · Sensors · doi:10.3390/s22135041 · Cited by 29
Association of Daily Rest-Activity Patterns With Adiposity and Cardiometabolic Risk Measures in Teens
Quante, M., Feliciano, E. M. C., Rifas‐Shiman, S. L., Mariani, S., Kaplan, E. R., Rueschman, M., Oken, E., Taveras, E. M., & Redline, S. S. (2019) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2019.02.008 · Cited by 28
Self-citation
Changes in accelerometer-measured sleep during the transition to retirement: the Finnish Retirement and Aging (FIREA) study
Myllyntausta, S., Pulakka, A., Salo, P., Kronholm, E., Pentti, J., Vahtera, J., & Stenholm, S. (2020) · SLEEP · doi:10.1093/sleep/zsz318 · Cited by 28
Does investing in low-income urban neighborhoods improve sleep?
Dubowitz, T., Haas, A. C., Ghosh‐Dastidar, B., Collins, R. L., Beckman, R., Holliday, S. B., Richardson, A. S., Hale, L. E., Buysse, D. J., Buman, M. P., et al. (2021) · SLEEP · doi:10.1093/sleep/zsaa292 · Cited by 28
Sleep in the Hospitalized Child
Berger, J. A., Zaidi, M. A., Halferty, I., & Kudchadkar, S. R. (2021) · CHEST Journal · doi:10.1016/j.chest.2021.04.024 · Cited by 28
Sleep assessment by means of a wrist actigraphy-based algorithm: agreement with polysomnography in an ambulatory study on older adults
Regalia, G., Gerboni, G., Migliorini, M., Lai, M., Pham, J., Puri, N., Pavlova, M. K., Picard, R. W., Sarkis, R. A., & Onorati, F. (2020) · Chronobiology International · doi:10.1080/07420528.2020.1835942 · Cited by 27
Digital circadian and sleep health in individual hospital shift workers: A cross sectional telemonitoring study
Zhang, Y., Cordina‐Duverger, E., Komarzynski, S., Attari, A., Huang, Q., Aristizabal, G., Faraut, B., Léger, D., Adam, R. A., Guénel, P., et al. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104121 · Cited by 27
An automated heart rate-based algorithm for sleep stage classification: Validation using conventional polysomnography and an innovative wearable electrocardiogram device
Pini, N., Ong, J. L., Yılmaz, G., Chee, N. I. Y. N., Siting, Z., Awasthi, A., Biju, S., Kishan, K., Patanaik, A., Fifer, W. P., et al. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.974192 · Cited by 27
Sleep, evening light exposure and perceived stress in healthy nulliparous women in the third trimester of pregnancy
Liset, R., Grønli, J., Henriksen, R. E., Henriksen, T. E. G., Nilsen, R. M., & Pallesen, S. (2021) · PLoS ONE · doi:10.1371/journal.pone.0252285 · Cited by 27
Estimating sleep duration: performance of open-source processing of actigraphy compared to in-laboratory polysomnography in the community
Sansom, K., Reynolds, A. C., McVeigh, J. A., Mazzotti, D. R., Dhaliwal, S. S., Maddison, K. J., Walsh, J. H., Singh, B., Eastwood, P. R., & McArdle, N. (2023) · SLEEP Advances · doi:10.1093/sleepadvances/zpad028 · Cited by 30
Equivalency of Sleep Estimates: Comparison of Three Research-Grade Accelerometers
Plekhanova, T., Rowlands, A. V., Yates, T. E., Hall, A. P., Brady, E. M., Davies, M. J., Khunti, K., & Edwardson, C. L. (2020) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2019-0047 · Cited by 26
Lower Serum Calcium Levels Associated with Disrupted Sleep and Rest–Activity Rhythm in Shift Workers
Jeon, Y.-S., Yu, S., Kim, C., Lee, H. J., Yoon, I., & Kim, T. (2022) · Nutrients · doi:10.3390/nu14153021 · Cited by 26
Comparison of sleep parameters from wrist-worn ActiGraph and Actiwatch devices
Liu, F., Schrack, J. A., Wanigatunga, S., Rabinowitz, J. A., He, L., Wanigatunga, A. A., Zipunnikov, V., Simonsick, E. M., Ferrucci, L., & Spira, A. P. (2023) · SLEEP · doi:10.1093/sleep/zsad155 · Cited by 24
Measurement Methods of Fatigue, Sleepiness, and Sleep Behaviour Aboard Ships: A Systematic Review
Kerkamm, F., Dengler, D., Eichler, M., Materzok-Köppen, D., Belz, L., Neumann, F. A., Zyriax, B., Harth, V., & Oldenburg, M. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19010120 · Cited by 24
Physical Literacy for Communities (PL4C): physical literacy, physical activity and associations with wellbeing
Tang, Y., Algurén, B., Pelletier, C., Naylor, P., & Faulkner, G. (2023) · BMC Public Health · doi:10.1186/s12889-023-16050-7 · Cited by 33
Efficacy of Polygonatum sibiricum on Mild Insomnia: A Randomized Placebo-Controlled Trial
Ha, E., Hong, H., Kim, T. D., Hong, G., Lee, S. L., Kim, S., Kim, N., Jeon, S. D., Ahn, C.-W., Kim, H. J., et al. (2019) · Nutrients · doi:10.3390/nu11081719 · Cited by 23
Actigraphic estimates of sleep and the sleep-wake rhythm, and 6-sulfatoxymelatonin levels in healthy Dutch children
Rensen, N., Steur, L. M. H., Wijnen, N. E., Someren, E. J. W. V., Kaspers, G. J. L., & van Litsenburg, R. R. L. (2020) · Chronobiology International · doi:10.1080/07420528.2020.1727916 · Cited by 22
Sleep, a Governor of Morbidity in PTSD: A Systematic Review of Biological Markers in PTSD-Related Sleep Disturbances
Maguire, D. G., Ruddock, M. W., Milanak, M. E., Moore, T., Cobice, D., & Armour, C. (2020) · Nature and Science of Sleep · doi:10.2147/nss.s260734 · Cited by 26
Using GPS Technologies to Examine Community Mobility in Older Adults
Crane, B. M., Moored, K., Rosso, A. L., & Carlson, M. C. (2022) · The Journals of Gerontology Series A · doi:10.1093/gerona/glac185 · Cited by 22
Presleep α-Lactalbumin Consumption Does Not Improve Sleep Quality or Time-Trial Performance in Cyclists
MacInnis, M. J., Dziedzic, C. E., Wood, E., Oikawa, S. Y., & Phillips, S. M. (2020) · International Journal of Sport Nutrition and Exercise Metabolism · doi:10.1123/ijsnem.2020-0009 · Cited by 21
Impact of change in bedtime variability on body composition and inflammation: secondary findings from the Go Red for Women Strategically Focused Research Network
St‐Onge, M., Campbell, A., Zuraikat, F. M., Cheng, B., Shah, R., Berger, J. S., Sampogna, R. V., & Jelić, S. (2020) · International Journal of Obesity · doi:10.1038/s41366-020-0555-1 · Cited by 20
A meta-analysis of accelerometer sleep outcomes in healthy children based on the Sadeh algorithm: the influence of child and device characteristics
van Kooten, J. A. M. C., Jacobse, S. T. W., Heymans, M. W., de Vries, R., Kaspers, G. J. L., & van Litsenburg, R. R. L. (2020) · SLEEP · doi:10.1093/sleep/zsaa231 · Cited by 21
Validation Framework for Sleep Stage Scoring in Wearable Sleep Trackers and Monitors with Polysomnography Ground Truth
Nguyen, Q. N. T., Le, T. T., Huynh, Q. B. T., Setty, A. R., Toi, V. V., & Le, T. Q. (2021) · Clocks & Sleep · doi:10.3390/clockssleep3020017 · Cited by 20
A scoping review of sleep discrepancy methodology: What are we measuring and what does it mean?
Walton, T. F., Ree, M. J., Fueggle, S. N., & Bucks, R. S. (2024) · Sleep Medicine · doi:10.1016/j.sleep.2024.11.016 · Cited by 25
Assessing the effects of sleep on neurocognitive performance and injury rate in adolescent athletes using actigraphy
Patel, A. R., Hsu, A., Perez, I. A., Wren, T. A. L., & Edison, B. R. (2020) · Research in Sports Medicine · doi:10.1080/15438627.2020.1716229 · Cited by 21
Sleep and functional capacity in adults: Cross-sectional associations among self-report and objective assessments
Teas, E., & Friedman, E. M. (2021) · Sleep Health · doi:10.1016/j.sleh.2020.12.001 · Cited by 19
Consumer Wearable Sleep Trackers
Chiang, A. A.-P., & Khosla, S. (2023) · Sleep Medicine Clinics · doi:10.1016/j.jsmc.2023.05.005 · Cited by 19
Objective Measures of Sleep Apnea and Actigraphy-Based Sleep Characteristics as Correlates of Subjective Sleep Quality in an Epidemiologic Study: The Jackson Heart Sleep Study
Johnson, D. A., Javaheri, S., Guo, N., Champion, C., Sims, J. F., Brock, M. P., Sims, M., Patel, S. R., Williams, D. R., Wilson, J. G., et al. (2019) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000778 · Cited by 20
Self-citation
Sleep Disturbances, Changes in Sleep, and Cognitive Function in Low-Income African Americans
Troxel, W., Haas, A. C., Dubowitz, T., Ghosh‐Dastidar, B., Butters, M. A., Gary‐Webb, T. L., Weinstein, A. M., & Rosso, A. L. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-215530 · Cited by 19
Evaluation of the Accuracy of Contactless Consumer Sleep-Tracking Devices Application in Human Experiment: A Systematic Review and Meta-Analysis
Zhai, H., Yan, Y., He, S., Zhao, P., & Zhang, B. (2023) · Sensors · doi:10.3390/s23104842 · Cited by 19
Evidence That Sleep Is an Indicator of Overtraining during the Competition Phase of Adolescent Sprinters
Campbell, E., Poudevigne, M. S., McFarlane, S., Dilworth, L., & Irving, R. R. (2021) · Journal of Sports Medicine · doi:10.1155/2021/6694547 · Cited by 18
Firefighter sleep: a pilot study of the agreement between actigraphy and self-reported sleep measures
Billings, J. M. (2021) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.9566 · Cited by 19
Quantifying postoperative sleep loss associated with increased pain in children undergoing a modified Nuss operation
Many, B. T., Rizeq, Y. K., Kwon, S., Vacek, J. C., Goldstein, S. D., Hunter, C. J., Ghomrawi, H., & Abdullah, F. (2020) · Journal of Pediatric Surgery · doi:10.1016/j.jpedsurg.2019.12.003 · Cited by 17
Assessing the relationship between physical activity and the gut microbiome in a large, population-based sample of Wisconsin adults
Holzhausen, E. A., Malecki, K. M. C., Sethi, A. K., Gangnon, R. E., Cadmus‐Bertram, L., Deblois, C. L., Suen, G., Safdar, N., & Peppard, P. E. (2022) · PLoS ONE · doi:10.1371/journal.pone.0276684 · Cited by 18
Emerging applications of objective sleep assessments towards the improved management of insomnia
Scott, H., Lechat, B., Manners, J., Lovato, N., Vakulin, A., CATCHESIDE, P. G., Eckert, D. J., & Reynolds, A. C. (2022) · Sleep Medicine · doi:10.1016/j.sleep.2022.10.030 · Cited by 17
Comparison of actigraphy‐measured and parent‐reported sleep in association with weight status among preschool children
Abdollahi, A. M., Li, X., Merikanto, I., Leppänen, M. H., Vepsäläinen, H., Lehto, R., Ray, C., Erkkola, M., & Roos, E. (2023) · Journal of Sleep Research · doi:10.1111/jsr.13960 · Cited by 19
Daily coping moderates the relations between stress and actigraphic sleep: a daily intensive longitudinal study with ecological momentary assessments.
Yap, Y., Bei, B., & Wiley, J. (2021) · Sleep Medicine · doi:10.1016/j.sleep.2021.10.012 · Cited by 15
Using New Technologies and Wearables for Characterizing Sleep in Population-based Studies
Jaiswal, S. J., Pawelek, J., Warshawsky, S., Quer, G., Trieu, M., Pandit, J. A., & Owens, R. L. (2024) · Current Sleep Medicine Reports · doi:10.1007/s40675-023-00272-7 · Cited by 16
Sleep in Offspring of Parents With Mood Disorders
Wescott, D. L., Morash-Conway, J., Zwicker, A., Cumby, J., Uher, R., & Rusak, B. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00225 · Cited by 16
The influence of intensity and timing of daily light exposure on subjective and objective sleep in adolescents with an evening circadian preference
Gasperetti, C. E., Dolsen, E. A., & Harvey, A. G. (2020) · Sleep Medicine · doi:10.1016/j.sleep.2020.11.014 · Cited by 16
Sleep problems in children with Angelman Syndrome: The effect of a behavioral intervention program
Heus, K. B., Radstaake, M., Legerstee, J. S., ten Hoopen, L. W., Dieleman, G. C., Moll, H. A., Mous, S. E., & de Wit, M. Y. (2023) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2023.104444 · Cited by 16
Detecting Sleep and Nonwear in 24-h Wrist Accelerometer Data from the National Health and Nutrition Examination Survey
Thapa-Chhetry, B., ARGUELLO, D. J., John, D., & Intille, S. S. (2022) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002973 · Cited by 17
Paradoxical Effects of Prolonged Insufficient Sleep on Lipid Profile: A Pooled Analysis of 2 Randomized Trials
Barragán, R., Zuraikat, F. M., Cheng, B., Scaccia, S. E., Cochran, J., Aggarwal, B., Jelić, S., & St‐Onge, M. (2023) · Journal of the American Heart Association · doi:10.1161/jaha.123.032078 · Cited by 16
The impact of sleep loss on sustained and transient attention: an EEG study
Shenfield, L., Beanland, V., Filtness, A., & Apthorp, D. (2020) · PeerJ · doi:10.7717/peerj.8960 · Cited by 17
Sleep duration and physical performance during a 6‐week military training course
Edgar, D. T., Gill, N. D., Beaven, C. M., Zaslona, J. L., & Driller, M. W. (2021) · Journal of Sleep Research · doi:10.1111/jsr.13393 · Cited by 15
A clinical-translational review of sleep problems in neurodevelopmental disabilities
Peters, S. U., Shelton, A. R., Malow, B. A., & Neul, J. L. (2024) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-024-09559-4 · Cited by 15
Short-term efficacy of home-based heart rate variability biofeedback on sleep disturbance in patients with incurable cancer: a randomised open-label study
Hasuo, H., Kanbara, K., Shizuma, H., Morita, Y., & Fukunaga, M. (2020) · BMJ Supportive & Palliative Care · doi:10.1136/bmjspcare-2020-002324 · Cited by 14
The effect of the transdiagnostic sleep and circadian intervention (TranS-C) on actigraphic estimates of sleep and rest-activity rhythms in adolescents with an evening circadian preference
Gasperetti, C. E., Dong, L., & Harvey, A. G. (2021) · Sleep Health · doi:10.1016/j.sleh.2021.10.007 · Cited by 14
Seasonal Variation in Diurnal Rhythms of the Human Eye: Implications for Continuing Ocular Growth in Adolescents and Young Adults
Nilsen, N. G., Gilson, S. J., Pedersen, H. R., Hagen, L. A., Kenneth Knoblauch, B., & Baraas, R. C. (2022) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.63.11.20 · Cited by 14
Sleep profiles of Australian children aged 11–12 years and their parents: sociodemographic characteristics and lifestyle correlates
Matricciani, L., Paquet, C., Fraysse, F., Wake, M. A., & Olds, T. (2020) · Sleep Medicine · doi:10.1016/j.sleep.2020.04.017 · Cited by 16
Sleep-Wake Disturbances and Episodic Memory in Older Adults
Yeh, A.-Y., Pressler, S. J., Algase, D. L., Struble, L. M., Pozehl, B., Berger, A. M., & Giordani, B. (2020) · Biological Research For Nursing · doi:10.1177/1099800420941601 · Cited by 14
Assessing the daily association of sleep hygiene behaviours with sleep: A between and within persons approach
McAlpine, T., Mullan, B. A., & Clarke, P. (2023) · Journal of Behavioral Medicine · doi:10.1007/s10865-023-00448-0 · Cited by 17
Hormone-sleep interactions predict cerebellar connectivity and behavior in aging females
Ballard, H. K., Jackson, T. B., Hicks, T. H., Cox, S. J., Symm, A. C., Maldonado, T., & Bernard, J. A. (2022) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2023.106034 · Cited by 14
Sleep duration and perceptions of sleep quality in British Army recruits during basic training - an observational analysis
Rawcliffe, A. J., Tyson, H., Hinde, K., Jacka, K., Holland, R., Chapman, S., & Roberts, A. J. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1321032 · Cited by 12

Authors: Quante, M., Kaplan, E. R., Cailler, M., Rueschman, M., Wang, R., Weng, J., Taveras, E. M., & Redline, S.
Affiliations: Brigham and Women's Hospital (US)
Harvard University (US)
Corresponding author: Quante, Mirja
ORCID: Quante, Mirja
Kaplan, Emily R
Rueschman, Michael
Wang, Rui
Weng, Jia
Taveras, Elsie M
Redline, Susan
Venue: Nature and Science of Sleep
Publisher: Informa UK Limited
Published: 2018-01
Volume: Volume 10
Pages: 13-20
ISSN: 1179-1608 (ISSN-L)
DOI: 10.2147/nss.s151085
PMC: PMC5779275
OpenAlex: W2783706351
Cited by: 298
References: 22
Citations by year: 2026: 35 · 2025: 27 · 2024: 53 · 2023: 41 · 2022: 42 · 2021: 41 · 2020: 31 · 2019: 19 · 2018: 8
FWCI: 32.40
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc
creativecommons.org/licenses/by-nc/3.0/ (unspecified)
Locations: Nature and Science of Sleep (publishedVersion, cc-by-nc)
Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
PubMed Central (publishedVersion, cc-by-nc)
PubMed (publishedVersion)
Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
Dove Medical Press (Taylor and Francis Group) (submittedVersion, cc-by-nc)
Funders: Max Kade Foundation
National Cancer Institute (U54CA155626)
Topics: Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
Life Sciences › Neuroscience › Endocrine and Autonomic Systems › Circadian rhythm and melatonin
Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and Work-Related Fatigue
Keywords: actigraphy, polysomnography, total sleep time, wake after sleep onset, adolescents, adults, device agreement, home sleep monitoring

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Actigraphy-based sleep estimation in adolescents and adults: a comparison with polysomnography using two scoring algorithms

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks