The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Further Validation of Actigraphy for Sleep Studies
Open accessAbstract
STUDY OBJECTIVES: Actigraphy is generally compared to polysomnography (PSG), which has been considered the gold standard for sleep studies. The objective of the present study was to evaluate the concordance between PSG and two previously proposed algorithms (Cole et al, 1992; Sadeh et al, 1994) to analyze actigraphic recordings. The minute-by-minute agreement rate was evaluated through calculation of sensitivity, specificity, and accuracy. Regarding the sleep parameters, the concordance was performed through the Bland and Altman technique. DESIGN: A night of adaptation to the sleep laboratory followed by simultaneous polysomnographic and actigraphic recordings throughout the night. PARTICIPANTS: 21 healthy volunteers. SETTING: A sleep laboratory INTERVENTIONS: None. RESULTS: Ninety-one percent of all PSG epochs were correctly identified by both algorithms, and this accuracy is reasonably satisfactory. The actigraphy was a sensitive method, with values of 99% and 97% for Cole's and Sadeh's algorithms, respectively. However, actigraphy had a low specificity: 34% and 44% for Cole's and Sadeh's algorithms, respectively. The Bland and Altman technique showed that actigraphy systematically overestimated Sleep Latency, Total Sleep Time and Sleep Efficiency while it underestimated Intermittent Awakenings. CONCLUSIONS: The results of this study show the utility of actigraphy as a useful method for assessment of sleep, despite its limitations regarding identification of waking epochs during sleep. The Bland and Altman concordance technique was revealed to be a powerful tool to evaluate how well actigraphy agreed with polysomnography. This technique, combined with calculations of sensitivity and specificity, appears to be the most adequate procedure for the assessment of concordance.
Overview
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
Cites
Statistics
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research
Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R., & Kupfer, D. J. (1989) · Psychiatry Research · doi:10.1016/0165-1781(89)90047-4 · Cited by 35,234
Cited by
Self-Reported and Measured Sleep Duration: How Similar Are They?
Lauderdale, D. S., Knutson, K. L., Yan, L. L., Liu, K., & Rathouz, P. J. (2008) · Epidemiology · doi:10.1097/ede.0b013e318187a7b0 · Cited by 1,565
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
Morgenthaler, T., Alessi, C., Friedman, L., Owens, J., Kapur, V., Boehlecke, B., Brown, T., Chesson, A., Coleman, J., Lee-Chiong, T., et al. (2007) · Sleep · doi:10.1093/sleep/30.4.519 · Cited by 1,142
Marino, M., Li, Y., Rueschman, M. N., Winkelman, J. W., Ellenbogen, J. M., Solet, J. M., Dulin, H., Berkman, L. F., & Buxton, O. M. (2013) · Sleep · doi:10.5665/sleep.3142 · Cited by 1,056
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
Short sleep duration in middle childhood: risk factors and consequences
Nixon, G. M., Thompson, J. M. D., Han, D. Y., Becroft, D. M., Clark, P. M., Robinson, E., Waldie, K. E., Wild, C. J., Black, P. N., & Mitchell, E. A. (2008) · SLEEP · doi:10.1093/sleep/31.1.71 · Cited by 414
Lack of sleep and unethical conduct
Barnes, C. M., Schaubroeck, J., Huth, M., & Ghumman, S. (2011) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2011.01.009 · Cited by 330
Movement toward a novel activity monitoring device
Montgomery-Downs, H. E., Insana, S. P., & Bond, J. A. (2012) · Sleep and Breathing · doi:10.1007/s11325-011-0585-y · Cited by 301
Quante, M., Kaplan, E. R., Cailler, M., Rueschman, M., Wang, R., Weng, J., Taveras, E. M., & Redline, S. (2018) · Nature and Science of Sleep · doi:10.2147/nss.s151085 · Cited by 298
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
Walch, O., Huang, Y., Forger, D., & Goldstein, C. (2019) · Sleep · doi:10.1093/sleep/zsz180 · Cited by 240
de Zambotti, M., Baker, F. C., Willoughby, A. R., Godino, J. G., Wing, D., Patrick, K., & Colrain, I. M. (2016) · Physiology & Behavior · doi:10.1016/j.physbeh.2016.03.006 · Cited by 222
Circadian eating and sleeping patterns in the night eating syndrome
O'Reardon, J. P., Ringel, B. L., Dinges, D. F., Allison, K. C., Rogers, N. L., Martino, N. S., & Stunkard, A. J. (2004) · Obesity Research · doi:10.1038/oby.2004.222 · Cited by 223
Basner, M., Dinges, D. F., Mollicone, D., Ecker, A., Jones, C. W., Hyder, E. C., Di Antonio, A., Savelev, I., Kan, K., Goel, N., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1212646110 · Cited by 183
DoppleSleep: a contactless unobtrusive sleep sensing system using short-range Doppler radar
Rahman, T., Adams, A. T., Ravichandran, R. V., Zhang, M., Patel, S. N., Kientz, J. A., & Choudhury, T. (2015) · Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2750858.2804280 · Cited by 175
Alternatives to polysomnography (PSG): a validation of wrist actigraphy and a partial-PSG system
Kosmadopoulos, A., Sargent, C., Darwent, D., Zhou, X., & Roach, G. D. (2014) · Behavior Research Methods · doi:10.3758/s13428-013-0438-7 · Cited by 160
Tiede, W., Magerl, W., Baumgärtner, U., Durrer, B., Ehlert, U., & Treede, R.-D. (2010) · Pain · doi:10.1016/j.pain.2009.08.029 · Cited by 153
Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor
Nakamura, T., Alqurashi, Y. D., Morrell, M. J., & Mandic, D. P. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2911423 · Cited by 147
Sleep and wake classification with ECG and respiratory effort signals
Karlen, W., Mattiussi, C., & Floreano, D. (2009) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2008.2008817 · Cited by 134
Evaluation of a consumer fitness-tracking device to assess sleep in adults
de Zambotti, M., Claudatos, S., Inkelis, S., Colrain, I. M., & Baker, F. C. (2015) · Chronobiology International · doi:10.3109/07420528.2015.1054395 · Cited by 127
HJORTH, M. F., CHAPUT, J.-P., DAMSGAARD, C. T., DALSKOV, S.-M., MICHAELSEN, K. F., TETENS, I., & SJÖDIN, A. (2012) · Sleep and Biological Rhythms · doi:10.1111/j.1479-8425.2012.00578.x · Cited by 106
Benchmark on a large cohort for sleep-wake classification with machine learning techniques
Palotti, J., Mall, R., Aupetit, M., Rueschman, M., Singh, M., Sathyanarayana, A., Taheri, S., & Fernandez-Luque, L. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0126-9 · Cited by 104
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
Zero-Effort In-Home Sleep and Insomnia Monitoring using Radio Signals
Hsu, C.-Y., Ahuja, A., Yue, S., Hristov, R., Kabelac, Z., & Katabi, D. (2017) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3130924 · Cited by 97
Chen, H.-M., Tsai, C.-M., Wu, Y.-C., Lin, K.-C., & Lin, C.-C. (2016) · British Journal of Cancer · doi:10.1038/bjc.2016.356 · Cited by 89
Sleeping for Two: An Open-Pilot Study of Cognitive Behavioral Therapy for Insomnia in Pregnancy
Tomfohr-Madsen, L. M., Clayborne, Z. M., Rouleau, C. R., & Campbell, T. S. (2017) · Behavioral Sleep Medicine · doi:10.1080/15402002.2016.1141769 · Cited by 86
Deshpande, A., Irani, N., Balkrishnan, R., & Benny, I. R. (2020) · Sleep Medicine · doi:10.1016/j.sleep.2020.03.012 · Cited by 78
Mazzotti, D. R., Guindalini, C., Moraes, W. A. D. S., Andersen, M. L., Cendoroglo, M. S., Ramos, L. R., & Tufik, S. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00134 · Cited by 72
Self-citationCapturing sleep–wake cycles by using day-to-day smartphone touchscreen interactions
Borger, J. N., Huber, R., & Ghosh, A. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0147-4 · Cited by 64
Wainberg, M., Jones, S. E., Beaupre, L. M., Hill, S. L., Felsky, D., Rivas, M. A., Lim, A. S. P., Ollila, H. M., & Tripathy, S. J. (2021) · PLOS Medicine · doi:10.1371/journal.pmed.1003782 · Cited by 64
Worried sleep: 24-h monitoring in high and low worriers
Weise, S., Ong, J., Tesler, N. A., Kim, S., & Roth, W. T. (2013) · Biological Psychology · doi:10.1016/j.biopsycho.2013.04.009 · Cited by 62
Haghayegh, S., Khoshnevis, S., Smolensky, M. H., Diller, K. R., & Castriotta, R. J. (2019) · Chronobiology International · doi:10.1080/07420528.2019.1679826 · Cited by 38
Home-Based Behavioral Sleep Training for Shift Workers: A Pilot Study
Lee, K. A., Gay, C. L., & Alsten, C. R. (2014) · Behavioral Sleep Medicine · doi:10.1080/15402002.2013.825840 · Cited by 35
Sleep disturbance in persistent allergic rhinitis measured using actigraphy
Rimmer, J., Downie, S., Bartlett, D. J., Gralton, J., & Salome, C. (2009) · Annals of Allergy, Asthma & Immunology · doi:10.1016/s1081-1206(10)60180-9 · Cited by 32
No phenotypic or genotypic evidence for a link between sleep duration and brain atrophy
Fjell, A. M., Sørensen, Ø., Wang, Y., Amlien, I. K., Baaré, W. F. C., Bartrés-Faz, D., Bertram, L., Boraxbekk, C.-J., Brandmaier, A. M., Demuth, I., et al. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01707-5 · Cited by 29
Sleep and mental health in recruit paramedics: a 6-month longitudinal study
Nguyen, E., Meadley, B., Harris, R., Rajaratnam, S. M. W., Williams, B., Smith, K., Bowles, K.-A., Dobbie, M. L., Drummond, S. P. A., & Wolkow, A. P. (2023) · SLEEP · doi:10.1093/sleep/zsad050 · Cited by 27
Tonetti, L., Fábregas, S. E., Fabbri, M., Occhionero, M., Erbacci, A., Martoni, M., & Natale, V. (2013) · Biological Rhythm Research · doi:10.1080/09291016.2012.692252 · Cited by 7
James, L., Smart, D., Odom-Maryon, T., Honn, K. A., & Rowan, S. (2019) · Military Psychology · doi:10.1080/08995605.2019.1565909 · Cited by 5
Metadata
- Authors
- de Souza, L., Benedito-Silva, A. A., Pires, M. L. N., Poyares, D., Tufik, S., & Calil, H. M.
- Venue
- Sleep
- Publisher
- Oxford University Press (OUP)
- Published
- 2003-01
- Volume
- 26
- Issue
- 1
- Pages
- 81-85
- ISSN
- 0161-8105 (ISSN-L)
- 1550-9109
- OpenAlex
- W202036063
- Cited by
- 587
- References
- 1
- FWCI
- 8.34
- Type
- journal article
- Language
- en
- Open access
- green
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
