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Action 4 software

Manufactured by Ambulatory Monitoring
Sourced in United States

Action 4 software is a data acquisition and analysis tool for ambulatory monitoring applications. It provides core functions to collect, process, and visualize physiological data from various sensors and devices.

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8 protocols using Action 4 software

Participants were instructed to record their sleep-wake pattern for a week using a sleep diary (Carney et al., 2012 (link)). They also wore an actigraph (Micro Motionloggers; Ambulatory Monitoring, Inc., Ardsley, NY) on their non-dominant hand throughout the study protocol as an objective measure correlated with sleep. Participants were asked to press the event marker before going to bed. Actigraphic data were acquired in 1 min bins using the zero-crossing mode and Action 4 software (Ambulatory Monitoring Inc.) and scored as sleep or wake based on a validated algorithm (Sadeh et al., 1994 (link)), and with reference to sleep diary data. Variables of interest included total sleep time (TST), sleep onset latency (SOL) and sleep efficiency (SE). TST was calculated by deducting SOL and wake after sleep onset (WASO) from time in bed. SE was calculated as TST divided by TIB.
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Actigraphy data were interpreted with autoscoring programs (Action4 software [Ambulatory Monitoring Inc]). Data were double entered into an Access 2007 (Microsoft Corporation) database and analyzed in SAS, version 9.1 (SAS Institute), to obtain the mean minutes of nighttime sleep, total sleep in 24 hours, number of nighttime awakenings, and longest stretch of nighttime sleep. To describe baseline characteristics and data related to sleep quantity and sleep patterns, frequencies (%) were used for categorical variables and means (SD) or medians (interquartile range [IQR]) for continuous variables. Finally, data from the light and sound meters were analyzed to obtain the mean minutes of light greater than 150 lux, minutes of sound greater than 46 dB, and minutes of sound greater than 80 dB. A generalized estimating equation was used to evaluate associations between minutes of nighttime sleep with child, treatment, and environmental variables, accounting for clustering within child to account for repeated measures across nights. A Cox proportional hazards model was used to evaluate hazards of nocturnal waking. Two-sided tests with a level of significance of P = .05 were used.
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The Action 4 software (Ambulatory Monitoring, Inc., Ardsley, NY, USA) was used in order to extract the minute-by-minute raw motor activity over 24 h for each working day. Then, for each participant, the mean of all working days was computed, allowing us to describe the raw circadian activity profile.
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4

Tracking Cancer Patients' Body Weight, Symptoms, and Sleep-Wake Patterns

The daily percentage of body weight change was calculated with reference to baseline values obtained over at least three days before the initial course of on-study chemotherapy. The 19 MDASI item scores were used without any predefined threshold. The rest-activity pattern was analyzed using the Action 4 software (Ambulatory Monitoring Inc). The dichotomy index Ilink)-31 (link)]. Ilink)]. A normal dichotomy index is one approaching 100%, indicating restful sleep in bed at night and regular and lively activity during the day, out of bed. Values of Ilink),33 (link)]. Here, IDescriptive analyses were performed for the overall distribution and individual longitudinal patterns of body weight change, of the 19 MDASI items separately, and of I
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For each working day, we extracted the raw motor activity counts, minute-by-minute over the 24 h, through the Action 4 software (Ambulatory Monitoring, Inc., Ardsley, NY, USA). Then, for each patient, we computed the mean profile of raw 24-h motor activity pattern.
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Sleep variables were based on adolescents’ sleep actigraphy data. Adolescents were instructed to wear the actigraph watch on their non-dominant hand and to keep it on for the duration of the time they spent in bed at night. An event marker was pressed to indicate when adolescents turned off the lights to go to bed, when they woke up, and when/if they got out of bed during the night. Data were scored using software package Action 4 (Ambulatory Monitoring, Inc.; Ardsley, NY). Sleep statistics for each night were calculated using one-minute epochs and the Sadeh actigraph scoring algorithm, which has been validated and used in studies with children and adolescents (15 ,34 ). Time in bed began when the adolescent pressed the event marker indicating lights out and ended when pressed again the next morning. Sleep onset began after at least 3 consecutive minutes of sleep were scored, and sleep offset was determined after the last five or more consecutive minutes of sleep. A total of 343 participants completed sleep actigraphy measurement during at least one study wave, 206 completed actigraphy for at least two waves, and 101 completed actigraphy for all three waves. During data collection periods, adolescents on average wore the watch for 6.19 nights. Descriptive statistics on sleep parameters are reported in Table 1.
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Wrist actigraphs (Micro Motionlogger, Ambulatory Monitoring, Inc., Ardsley, NY) were placed on both wrists for each patient and left in place for the duration of their stay in the NCCU or SU. Actigraphs were otherwise only purposefully removed in anticipation of magnetic resonance imaging scans as a safety precaution and were then replaced thereafter.
Each actigraph was configured to collect activity data in 1-min epochs. These data were then aggregated by proprietary algorithms from the Action4 software package (Ambulatory Monitoring, Inc.) into two distinct measurements: Zero Crossing Mode (ZCM), which measures the frequency of movement by counting the number of times per epoch that the signal crosses a threshold set near zero; and Proportional Integration Mode (PIM), which calculates the area under the curve for the acceleration signal during each epoch, and therefore discriminates between different intensities of motion. Data were subsequently downloaded using the Action W-2 software package (Ambulatory Monitoring, Inc.). A sample of a patient's actigraph data is provided in Supplementary material 1.
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Actigraphy data were processed using one-minute epochs and the Sadeh scoring algorithm in the software package Action 4 (Ambulatory Monitoring, Inc.; Ardsley, NY).2 (link),14 (link),29 (link)–31 (link) Sleep duration was total hours scored as sleep during adolescents’ in-bed period, which began at the time of the first event marker indicating when participants turned off the lights to go to sleep, and ended at the time of the last event marker indicating when they got out of bed in the morning. Weekday sleep durations (Sunday–Thursday nights) were averaged to compute participants’ mean weekday sleep duration. Friday and Saturday night sleep durations were averaged to compute their mean weekend sleep duration.
Nightly variability in sleep duration was calculated by taking the mean of the absolute differences between a participant’s mean nightly sleep duration and each individual night’s sleep duration.2 (link),14 (link),32 (link) Variability in weekday vs. weekend duration was computed by taking the absolute difference between a participant’s mean weekday and weekend sleep duration (weekday/weekend variability).
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