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  • br Background In such nonparametric variables were primarily


    Background In 1990, such nonparametric variables were primarily proposed by Witting et al., who had studied the effect of age and Alzheimer׳s disease on rest–activity rhythm [12]. These variables quantify the main characteristics of the rest–activity circadian rhythm, such as intradaily variability (IV), which quantifies the rhythm fragmentation; interdaily stability (IS), which quantifies the synchronization to the 24-h light–dark cycle; the average activity during the least active 5-h period, or nocturnal activity (L5); and the average activity during the most active 10-h period, or daily activity (M10). Rhythmic fragmentation and synchronization are measured, respectively, by IV and IS. Intradaily variability quantifies the frequency and extent of transitions between periods of rest and activity on an hourly basis [12,10,11]. High IV values indicate the occurrence of daytime naps and/or nocturnal activity episodes. Interdaily stability quantifies rhythm׳s synchronization to zeitgeber׳s 24-h day–night (or light–dark) cycle. Studies have shown that IV is an excellent variable for analysis, as it serves as a marker of sleep–wake ras pathway disturbances [6]. Assessment of interdaily variability in an elderly population shows a more fragmented rest–activity rhythm (high IV values) [6]. Researchers have also observed higher values of IV in patients with Alzheimer׳s disease when compared to controls [12,5]. Aging and Alzheimer׳s disease are factors that contribute to the degeneration of the suprachiasmatic nucleus [9,13], which may explain rhythm fragmentation. Furthermore, it was demonstrated that high IV (high rhythm fragmentation) is associated with decreased sleep quality [3], decreased cognitive functions [7] and decreased circadian rhythm amplitude [12,10]. On the other hand, high IS values indicate good synchronization of zeitgeber׳s 24h cycle, and good operation of the circadian timing system׳s (CTS) components, which are connected to photic and nonphotic synchronizations. This synchronization can be influenced by age, neurological disorders, and lifestyle. In terms of aging, the synchronization to zeitgeber׳s cycle increases the CTS maturity level [14]. The rhythm stability measured by IS has a direct relationship with quality of life measures. Studies have shown that IS is directly related to rhythm amplitude and ras pathway light exposure [12,10], Mini Mental State Examination [4], and sleep quality [3]. A well synchronized rhythm is associated with less fragmentation, less nocturnal activity, and better cognitive, behavioral, and emotional functioning [12,4]. Studies using the nonparametric approach, such as those cited above, have calculated the fragmentation and stability of rhythm using a 1h interval for analysis. However, new actimetry sensor models have been developed, and with the increase in storage capacity, limitations on sampling rates have been overcome. Now that current actimetry sensors are able to record data at a variety of intervals instead of only 1h, it is possible for rhythm fragmentation data to be analyzed at different intervals. For this reason, we propose a new method of quantifying fragmentation and synchronization data by extracting sampling intervals from 1min to 60min. In our study, we used a simulated time series of human and experimental animal rest–activity records obtained by the use of three different devices.
    Discussion It was not possible to detect rhythm fragmentation in patients with cerebrovascular disease using the classical IV calculation with a 60min sampling. However, when using different sampling rates, it was observed that patient rhythm was more fragmented than in the control group. As in other studies [10], the use of variable IV60 was not efficient to detect differences in sleep fragmentation in both groups. We propose that by utilizing only the IV calculated for the 60min interval, one may lose the sensitivity needed to determine rhythm fragmentation. The new method appears to be more sensitive to rhythm fragmentation. Moreover, by calculating IVm and IVerror, significant differences were observed between the groups, with the IV60 p-value approximately 14 times greater than that calculated for IVm and IVerror. This greater sensitivity shall encourage the use of this new method. Furthermore, to our knowledge there are no reports demonstrating that IV calculated by hourly sampled data is the best method to identify rhythm fragmentation.