The limited variability (consistency across days) in combination with the floor effect indicates that a 1-day data collection period is adequate. Little variability (+/-0.11 counts/min) in the level of physical activity was found across 1-4 days. Activity counts were very low, with over 50% of waking time spent in the 'very low' intensity category, demonstrating a marked floor effect. The variability of measurement, over 1, 2, 3 and 4 days, was examined using these weighted and rolling averages. The daily percent of waking time spent at each intensity level was calculated and a weighted average calculated to determine a single daily measure of activity. Activity counts were determined using Actigraph Standard Software. Activity levels were measured using the Actigraph over four consecutive weekdays. Four volunteers with rheumatoid arthritis participated in the study. This study investigates measurement issues when using the Actigraph motion sensor to measure the physical activity of people with disabilities. In order to arrive at both reliable and valid interpretations, these complications need to be carefully elaborated in future research. However, results may, besides contextual aspects, also substantially depend on specific methodological choices. To scrutinize actigraphic signal characteristics and especially their (deviations from) fractal scaling may be a useful tool for aiding diagnosis, characterization, and monitoring of dementia. In contrast, analysis of CDDs results in measures which highly fluctuate with respect to the time resolution of the assessed data which affects also further derived quantities such as scaling exponents or associations with other (clinically relevant) assessed parameters. DFA provides robust measures for the observed break-down of fractal scaling. Moreover, associations between these parameters and scores from the Mini-Mental-State-Examination and circadian activity parameters are explored.īoth analyses yield significant deviations from (mono-)fractal scaling over the entire considered time range. The impact of the used time resolution for data acquisition on the assessed fractal outcome parameters is particularly investigated. Two conceptually different fractal analyses, i.e., detrended fluctuation analysis (DFA) and analysis of cumulative distributions of durations (CDDs), are applied to actigraphy data of 36 geriatric in-patients diagnosed with dementia. However, methodological issues need to be cautiously taken into account in order to be able to provide reliable as well as valid interpretations of such signal analyses. Neurodegenerative diseases provide a natural framework to evaluate this paradigm when this cooperative function declines. Evidence has been accumulating that such fractal scaling is basically a consequence of interaction-dominant feedback mechanisms that cooperatively generate those signals. Many physiological signals yield fractal characteristics, i.e., finer details at higher magnifications resemble details of the whole. Such a difference was identified neither by the DFA nor WTMM method.Ĭonclusions: CFS patients had more abrupt interruptions of voluntary physical activity during diurnal periods in normal daily life, probed by the decreased correlation in the negative modulus maxima of the wavelet-transformed activity data, possibly due to their exaggerated fatigue. The WTNMM method revealed that, in diurnal activities, CFS patients had significantly (p <0.01) smaller fractal scaling exponent (0.87 ± 0.03) compared to controls (1.01 ± 0.03). No group difference was found in nocturnal activities. Results: Both for CFS and CON, we found the fractal time structures in their diurnal physical activity records for at least up to 35 minutes. We compared the fractal scaling exponents for CFS and CON by each method. Thus, we further developed a new method, the wavelet transform negative modulus maxima (WTNMM) method, which could evaluate the temporal correlation at the interruption of activities. We hypo-thesized that, due to their illness- and/or fatigue-induced resting episodes, altered physical activity patterns in CFS patients might be observed at the interruption of activity bursts. Methods: Fractal scaling exponents of diurnal and nocturnal physical activity time series in 10 CFS patients and 6 healthy control subjects (CON) were calculated by the detrended fluctuation analysis (DFA) and the wavelet transform modulus maxima (WTMM) method. Objectives: Our objectives were to study the temporal correlation of physical activity time series in patients with chronic fatigue syndrome (CFS) during normal daily life and to examine if it could identify the altered physical activity in these patients.
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