Micro motionlogger

Manufactured by Ambulatory Monitoring

The Micro Motionlogger is a compact and portable wearable device designed to monitor physical activity and movement. It is capable of recording data related to motion, including acceleration, orientation, and step count. The device is intended for use in various applications where objective measurement of physical activity is required.

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Lab products found in correlation

Action w2, by Ambulatory Monitoring (1 mentions) Action 4, by Ambulatory Monitoring (1 mentions)

5 protocols using micro motionlogger

Wrist-Worn Accelerometer Monitoring

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Participants were provided with an accelerometer (Micro Motionlogger; Ambulatory Monitoring Inc, Ardsley, NY) to be worn on the non-dominant wrist 24h/d, except for during water-based activities (i.e., showering), for 14 consecutive days and nights. Data were collected in zero-crossing mode and were stored in 60 s epochs. To be included in final rest-activity analyses, ≥10 d of accelerometry with >1000 min of data/d were required. All actigraphy data were visually screened and analyzed by the same researcher. Since all participants were college students, monitoring procedures were performed during the school semester when all participants were enrolled in a full-time course load and excluded any periods when students were on holiday breaks or during final examinations.

Hoopes E.K., Witman M.A., D’Agata M.N., Berube F.R., Brewer B., Malone S.K., Grandner M.A, & Patterson F. (2021). Rest-Activity Rhythms in Emerging Adults: Implications for Cardiometabolic Health. Chronobiology international, 38(4), 543-556.

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Objective Sleep Monitoring in Adolescents

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Participants wore a wrist-mounted actigraph (Micro Motionlogger©, Ambulatory Monitoring, Inc.) throughout the sleep protocol to gather an objective measure of sleep. At each Friday assessment, actigraph data were downloaded and both the actigraphy data and sleep diaries (described next) were reviewed with the adolescent and their parent. In tandem with visually inspecting the sleep diaries, a validated algorithm^{30 (link)} was used to obtain estimates of sleep onset, sleep offset, time in bed, sleep duration, sleep efficiency, and wake after sleep onset (WASO), which were then averaged into composite sleep variables for each week.

Becker S.P., Epstein J.N., Tamm L., Tilford A.A., Tischner C.M., Isaacson P.A., Simon J.O, & Beebe D.W. (2018). Shortened Sleep Duration Causes Sleepiness, Inattention, and Oppositionality in Adolescents With ADHD: Findings From a Crossover Sleep Restriction/Extension Study. Journal of the American Academy of Child and Adolescent Psychiatry, 58(4), 433-442.

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Wrist-Worn Accelerometer for Sleep-Wake and Physical Activity

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Participants wore an accelerometer (MicroMotionlogger; Ambulatory Monitoring Inc, Ardsley, NY) on the non-dominant wrist 24-h/day, except during water-based activities, for 14 consecutive days. Data were collected in zero-crossing mode and were stored in 60-s epochs. These devices and measurement methods are widely used for field-based estimates of sleep-wake and RAR and have been validated to polysomnography [19 (link),20 (link)]. To be included in final analyses, ≥10 days of accelerometry with >16-h of data/day were required [21 ]. All actigraphy data were visually screened and analyzed by the same researcher (see Supplemental Digital Content 1 for detailed description of data preprocessing methods).

HOOPES E.K., PATTERSON F., BERUBE F.R., D’AGATA M.N., BREWER B., MALONE S.K., FARQUHAR W.B, & WITMAN M.A. (2021). Actigraphy-Derived Rest-Activity Rhythms are Associated with Nocturnal Blood Pressure in Young Adult Females. Journal of hypertension, 39(12), 2413-2421.

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Actigraphy-Measured Sleep Patterns in PTSD

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Participants had their sleep-wake schedule monitored for seven nights with wrist actigraphy (Micro Motionlogger; Ambulatory Monitoring, Inc., Ardsley, NY). Actigraphy is an important objective estimate of sleep (31 (link)). Actigraphs were initialized and downloaded with the ActMe program (Ambulatory Monitoring, Inc., Ardsley, NY) using the ZCM sampling mode in one minute epochs. The Cole-Kripke algorithm was used in ActionW Version 2.7 (Ambulatory Monitoring, Inc., Ardsley, NY) software to estimate the sleep parameter of TST. Six participants in the control group did not have actigraphy data due to equipment malfunction (0 in the PTSD group), and a few participants in each group did not complete all seven nights. The mean number of nights for the PTSD group was 6.73 (SD = 0.87) and the mean number of nights for the control group was 6.52 (SD = 1.32). There was no significant difference between groups in the number of nights of actigraphy data.

Talbot L.S., Rao M.N., Cohen B.E., Richards A., Inslicht S.S., O’Donovan A., Maguen S., Metzler T.J, & Neylan T.C. (2015). Metabolic Risk Factors and Posttraumatic Stress Disorder: The Role of Sleep in Young, Healthy Adults. Psychosomatic medicine, 77(4), 383-391.

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Actigraph Monitoring in Neurological Care

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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.

Ahmed A., Garcia-Agundez A., Petrovic I., Radaei F., Fife J., Zhou J., Karas H., Moody S., Drake J., Jones R.N., Eickhoff C, & Reznik M.E. (2023). Delirium detection using wearable sensors and machine learning in patients with intracerebral hemorrhage. Frontiers in Neurology, 14, 1135472.

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