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      General spectral characteristics of human activity and its inherent scale-free fluctuations

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      , ,
      Scientific Reports
      Nature Publishing Group UK
      Signal processing, Power law, Scale invariance

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          Abstract

          The scale-free nature of daily human activity has been observed in different aspects; however, the description of its spectral characteristics is incomplete. General findings are complicated by the fact that—although actigraphy is commonly used in many research areas—the activity calculation methods are not standardized; therefore, activity signals can be different. The presence of 1/ f noise in activity or acceleration signals was mostly analysed for short time windows, and the complete spectral characteristic has only been examined in the case of certain types of them. To explore the general spectral nature of human activity in greater detail, we have performed Power Spectral Density (PSD) based examination and Detrended Fluctuation Analysis (DFA) on several-day-long, triaxial actigraphic acceleration signals of 42 healthy, free-living individuals. We generated different types of activity signals from these, using different acceleration preprocessing techniques and activity metrics. We revealed that the spectra of different types of activity signals generally follow a universal characteristic including 1/ f noise over frequencies above the circadian rhythmicity. Moreover, we discovered that the PSD of the raw acceleration signal has the same characteristic. Our findings prove that the spectral scale-free nature is generally inherent to the motor activity of healthy, free-living humans, and is not limited to any particular activity calculation method.

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          Most cited references62

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          Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations

          Accelerometers are widely used to measure sedentary time, physical activity, physical activity energy expenditure (PAEE), and sleep-related behaviors, with the ActiGraph being the most frequently used brand by researchers. However, data collection and processing criteria have evolved in a myriad of ways out of the need to answer unique research questions; as a result there is no consensus. Objectives The purpose of this review was to: (1) compile and classify existing studies assessing sedentary time, physical activity, energy expenditure, or sleep using the ActiGraph GT3X/+ through data collection and processing criteria to improve data comparability and (2) review data collection and processing criteria when using GT3X/+ and provide age-specific practical considerations based on the validation/calibration studies identified. Methods Two independent researchers conducted the search in PubMed and Web of Science. We included all original studies in which the GT3X/+ was used in laboratory, controlled, or free-living conditions published from 1 January 2010 to the 31 December 2015. Results The present systematic review provides key information about the following data collection and processing criteria: placement, sampling frequency, filter, epoch length, non-wear-time, what constitutes a valid day and a valid week, cut-points for sedentary time and physical activity intensity classification, and algorithms to estimate PAEE and sleep-related behaviors. The information is organized by age group, since criteria are usually age-specific. Conclusion This review will help researchers and practitioners to make better decisions before (i.e., device placement and sampling frequency) and after (i.e., data processing criteria) data collection using the GT3X/+ accelerometer, in order to obtain more valid and comparable data. PROSPERO registration number CRD42016039991.
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            Understanding individual human mobility patterns.

            Despite their importance for urban planning, traffic forecasting and the spread of biological and mobile viruses, our understanding of the basic laws governing human motion remains limited owing to the lack of tools to monitor the time-resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period. We find that, in contrast with the random trajectories predicted by the prevailing Lévy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time-independent characteristic travel distance and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that, despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent-based modelling.
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              The role of actigraphy in the study of sleep and circadian rhythms.

              In summary, although actigraphy is not as accurate as PSG for determining some sleep measurements, studies are in general agreement that actigraphy, with its ability to record continuously for long time periods, is more reliable than sleep logs which rely on the patients' recall of how many times they woke up or how long they slept during the night and is more reliable than observations which only capture short time periods. Actigraphy can provide information obtainable in no other practical way. It can also have a role in the medical care of patients with sleep disorders. However, it should not be held to the same expectations as polysomnography. Actigraphy is one-dimensional, whereas polysomnography comprises at least 3 distinct types of data (EEG, EOG, EMG), which jointly determine whether a person is asleep or awake. It is therefore doubtful whether actigraphic data will ever be informationally equivalent to the PSG, although progress on hardware and data processing software is continuously being made. Although the 1995 practice parameters paper determined that actigraphy was not appropriate for the diagnosis of sleep disorders, more recent studies suggest that for some disorders, actigraphy may be more practical than PSG. While actigraphy is still not appropriate for the diagnosis of sleep disordered breathing or of periodic limb movements in sleep, it is highly appropriate for examining the sleep variability (i.e., night-to-night variability) in patients with insomnia. Actigraphy is also appropriate for the assessment of and stability of treatment effects of anything from hypnotic drugs to light treatment to CPAP, particularly if assessments are done before and after the start of treatment. A recent independent review of the actigraphy literature by Sadeh and Acebo reached many of these same conclusions. Some of the research studies failed to find relationships between sleep measures and health-related symptoms. The interpretation of these data is also not clear-cut. Is it that the actigraph is not reliable enough to the access the relationship between sleep changes and quality of life measures, or, is it that, in fact, there is no relationship between sleep in that population and quality of life measures? Other studies of sleep disordered breathing, where actigraphy was not used and was not an outcome measure also failed to find any relationship with quality of life. Is it then the actigraph that is not reliable or that the associations just do not exist? The one area where actigraphy can be used for clinical diagnosis is in the evaluation of circadian rhythm disorders. Actigraphy has been shown to be very good for identifying rhythms. Results of actigraphic recordings correlate well with measurements of melatonin and of core body temperature rhythms. Activity records also show sleep disturbance when sleep is attempted at an unfavorable phase of the circadian cycle. Actigraphy therefore would be particularly good for aiding in the diagnosis of delayed or advanced sleep phase syndrome, non-24-hour-sleep syndrome and in the evaluation of sleep disturbances in shift workers. It must be remembered, however, that overt rest-activity rhythms are susceptible to various masking effects, so they may not always show the underlying rhythm of the endogenous circadian pacemaker. In conclusion, the latest set of research articles suggest that in the clinical setting, actigraphy is reliable for evaluating sleep patterns in patients with insomnia, for studying the effect of treatments designed to improve sleep, in the diagnosis of circadian rhythm disorders (including shift work), and in evaluating sleep in individuals who are less likely to tolerate PSG, such as infants and demented elderly. While actigraphy has been used in research studies for many years, up to now, methodological issues had not been systematically addressed in clinical research and practice. Those issues have now been addressed and actigraphy may now be reaching the maturity needed for application in the clinical arena.
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                Author and article information

                Contributors
                vadaig@inf.u-szeged.hu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                31 January 2024
                31 January 2024
                2024
                : 14
                : 2604
                Affiliations
                Department of Technical Informatics, University of Szeged, ( https://ror.org/01pnej532) 6720 Szeged, Hungary
                Article
                52905
                10.1038/s41598-024-52905-8
                10830482
                38297022
                07ea88d0-8080-4b85-81ab-5887136be96e
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 December 2023
                : 24 January 2024
                Funding
                Funded by: Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund
                Award ID: TKP2021-NVA-09
                Funded by: University of Szeged Open Access Fund
                Award ID: 6032
                Funded by: University of Szeged
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                © Springer Nature Limited 2024

                Uncategorized
                signal processing,power law,scale invariance
                Uncategorized
                signal processing, power law, scale invariance

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