title: Clinical application of a novel automatic algorithm for  actigraphy-based activity and rest period identification to  accurately determine awake and asleep ambulatory blood pressure parameters and cardiovascular risk.
abstract: This paper reports the results of a study designed to determine whether there are statistically significant differences between the values of ambulatory blood pressure monitoring (ABPM) parameters obtained using different methods-fixed schedule, diary, and automatic algorithm based on actigraphy-of defining the main activity and rest periods, and to determine the clinical relevance of such differences. We studied 233 patients (98 men/135 women), 61.29 ± .83 yrs of age (mean ± SD). Statistical methods were used to measure agreement in the diagnosis and classification of subjects within the context of ABPM and cardiovascular disease risk assessment. The results show that there are statistically significant differences both at the group and individual levels. Those at the individual level have clinically significant implications, as they can result in a different classification, and, therefore, different diagnosis and treatment for individual subjects. The use of an automatic algorithm based on actigraphy can lead to better individual treatment by correcting the accuracy problems associated with the fixed schedule on patients whose actual activity/rest routine differs from the fixed schedule assumed, and it also overcomes the limitations and reliability issues associated with the use of diaries.
authors.affiliation: Department of Electrical Engineering & Renewable Energy, Oregon Institute of Technology,27500 SW Parkway Ave., Wilsonville, OR 97070 USA. cristina.crespo@oit.edu
authors.forename: Cristina
authors.initials: C
authors.lastname: Crespo
authors.forename: José R
authors.initials: JR
authors.lastname: Fernández
authors.forename: Mateo
authors.initials: M
authors.lastname: Aboy
authors.forename: Artemio
authors.initials: A
authors.lastname: Mojón
mesh_terms: D056044:Actigraphy; D000368:Aged; D000465:Algorithms; D001331:Automation; D001794:Blood Pressure; D018660:Blood Pressure Monitoring, Ambulatory; D002318:Cardiovascular Diseases / Q000175:diagnosis; D002940:Circadian Rhythm; D001330:Electronic Data Processing; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012146:Rest; D012815:Signal Processing, Computer-Assisted; D012890:Sleep; D014851:Wakefulness