Motionlogger micro watch

Manufactured by Ambulatory Monitoring

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About the product

The Motionlogger Micro Watch is a compact, wearable device designed for activity monitoring. It provides objective data on physical movement and activity levels. The core function of the Motionlogger Micro Watch is to record and track physical motion and activity-related metrics.

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Spelling variants (same manufacturer)

Micro Motionlogger Sleep Watch - 30 citations Micro Motionlogger - 18 citations Motionlogger - 18 citations Motionlogger Watch - 9 citations Micro MotionLogger Watches - 2 citations Micro Motionlogger Sleep Watches - 2 citations Micro-Mini Motionlogger Watch (MM; - 2 citations

The spelling variants listed below correspond to different ways the product may be referred to in scientific literature.
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32 protocols using «motionlogger micro watch»

Actigraphy for Comprehensive Sleep Monitoring

2024

The Motionlogger Micro Watch from Ambulatory Monitoring, New York, USA (Version 1.99.5.1) was used to monitor sleep. The relevant sleep variables chosen for this trial were total night‐time sleep (TST), SOL, number of awakenings (NA) and WASO (for definitions of variables, see Section 2.8). An actigraph is an accelerometer that measures movement quantity and intensity over time (Berger et al., 2008 (link)). Sleep–wake patterns are detected by an examination of movement frequency and pattern by means of the validated Cole–Kripke scoring algorithm that classifies all motions as “awake” if they score above a predetermined threshold and “sleep” if they score below this threshold. Compared with polysomnography (PSG; the gold‐standard to measure sleep), the algorithm successfully differentiates between sleeping and waking 88% of the time (Cole et al., 1992 (link)). We used Zero Crossing (ZC) and Proportional Integrating Measure (PIM) modes for data activity gathering (Berger et al., 2008 (link); Cole et al., 1992 (link)). Light and temperature data were also collected as light may be used to confirm intended sleep time and waking, and a substantial drop in temperature may indicate that the actigraph has been detached.
All patients wore the actigraph for 24 hr a day for the consecutive 4 weeks (28 nights in total), including when taking showers or swimming as the equipment is waterproof. The actigraph was worn on the wrist of their non‐dominant arm. The start and stop intervals of sleep, including “intended sleep time” and “out of bed time”, were registered in sleep diaries and used to assist the actigraphic recordings (Kristiansen et al., 2020 (link)). In particular, recording the intended sleep time in sleep diaries was essential for calculating the patients' SOL.
The MotionLogger Analysis Software Action‐W (AW2; Version 2.7.2) was used to analyse the actigraphic sleep data.
The sleep scoring of actigraphic records followed the scoring rules described in the published protocol (Kristiansen et al., 2020 (link)) and in Supplementary File 1.

Kristiansen S.T., Videbech P., Speed M., Dionysopoulos P., Bjerrum M.B, & Larsen E.R. (2024). The efficacy of ball blankets on insomnia in depression in outpatient clinics: A randomised crossover multicentre trial. Journal of Sleep Research, 33(6), e14238.

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Actigraphy-based Sleep Assessment Protocol

2024

In the current study, the actigraph model Micro Motionlogger watch (Ambulatory Monitoring, Inc., Ardsley, NY, USA) was used. The hardware consists of a triaxial accelerometer presenting sensitivity equal to or higher than 0.01 g. The sampling frequency is 32 Hz, with the filters set to 2–3 Hz. The software Motionlogger Watchware (Ambulatory Monitoring, Inc., Ardsley, NY, USA) was used to initialize the actigraphs in zero crossing mode to collect motor activity data in 1 min epochs and to download the data onto a PC. Sleep was scored using Action W2.7.1150 software (Ambulatory Monitoring, Inc., Ardsley, NY, USA). Applying validated algorithms [40 ,41 (link)], each epoch was classified as sleep or wake. During the recorded period, participants were asked to fill in a sleep log daily within 30 min from the last morning awakening. Using both the event marker points and the information present in the sleep diaries, scoring was performed by an experienced scorer to determine the time spent in bed.

Occhionero M., Tonetti L., Giudetti F, & Natale V. (2024). Activity-Based Prospective Memory in Insomniacs. Sensors (Basel, Switzerland), 24(11), 3612.

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Evaluating Circadian Rhythms and Motor Activity

2024

Circadian preference. The circadian preference was assessed using the Italian version [22 (link)] of the rMEQ-CA. The rMEQ-CA comprises five items—two multiple choice and three open—extracted from the MEQ-CA [23 (link)]. Participants are asked to list their preferred time to get up, when they usually go to bed, whether they feel tired after waking up in the morning, when they reach their “best peak” during the day, and whether they identify as “morning” or “evening” profiles. The total score, ranging from 4 to 25, is produced by adding the scores of each item. Morning types fall into the range of 19 to 25, intermediate types fall between 11 and 18, and evening types fall between 4 and 10 on the overall score [22 (link)]. Excellent external validity is demonstrated by the questionnaire [22 (link)].
Motor activity. In order to evaluate circadian motor activity, adolescents were asked to wear a wrist actigraph (Micro Motionlogger Watch, Ambulatory Monitoring, Inc.; Ardsley, NY, USA) 24 h per day for a whole week. The hardware consists of a piezoelectric accelerometer with a sensitivity ≥0.01 g. The sampling frequency is 10 Hz, and filters are set to 2–3 Hz. The actigraph was initialized through the Motionlogger Watchware software (Ambulatory Monitoring, Inc., Ardsley, NY, USA) in zero-crossing mode to collect data in 1 min epochs. Adolescents were instructed to wear the actigraph for one week and to push the event marker button on the actigraph to indicate when they (a) turned off the lights to go to sleep at night, and (b) got out of bed in the morning. The actigraphic data were analyzed through Action W-2^® software, version 2.7.1 (Ambulatory Monitoring, Inc., Ardsley, NY, USA). This software identified each epoch as sleep or wake using the mathematical model validated by Cole and Kripke [24 ] and Cole and colleagues [25 (link)]. This mathematical model computed a weighted sum of the activity in the current epoch, the preceding 4 epochs, and the following 2 epochs as follows: S = 0.0033 (1.06an4 + 0.54an3 + 0.58an2 + 0.76an1 + 2.3a0 + 0.74a1 + 0.67a2), where an4 to an1 were the activity counts from the previous 4 min, and a1 and a2 were those related to the following 2 min. The current minute was scored as sleep when S < 1. Action W-2 underwent 5 additional re-scoring rules [26 (link)] developed to minimize the tendency of actigraphy to overestimate total sleep time. Participants were instructed to push the event-marker button on the device to mark occurrences such as time in and out of bed. During the recorded period, subjects were also asked to fill in the sleep log daily within 30 min of morning awakening. In this way, it was possible to identify the bedtime and the wake-up time even if the event mark button had not been pressed.
In the present study, we considered two actigraphic sleep parameters: bedtime and get-up time. The Action 4 software (Ambulatory Monitoring, Inc., Ardsley, NY, USA) was used to extract the raw motor activity counts, minute-by minute, in order to draw motor activity patterns around the wake–sleep and sleep–wake transition phases. In particular, we explored the raw motor activity pattern of the wake–sleep transition for each recorded day by extracting the motor activity counts from 120 min before bedtime up to 60 min after bedtime on school days only, and the raw motor activity pattern of the sleep–wake transition for each recorded day by extracting the motor activity counts from 60 min before get-up time up to 120 min after get-up time.

Natale V., Andreose A., Bacaro V., Giovagnoli S., Giudetti F., Grimaldi M., Tonetti L, & Crocetti E. (2024). Morningness–Eveningness Preference and Motor Wake–Sleep Inertia in Adolescents. Sensors (Basel, Switzerland), 24(23), 7668.

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Actigraphy for Sleep and Motor Activity

2024

The actigraph model Micro Motionlogger Watch (Ambulatory Monitoring Inc., Ardsley, NY, USA) was used in the present study to measure motor activity directly, and sleep indirectly, using algorithms validated through polysomnography [13 ,14 (link)]. The validity of actigraphy in sleep assessment has been quite extensively documented [15 (link),16 (link),17 (link)].
The software Watchware (version 1.99.34.1; Ambulatory Monitoring Inc., Ardsley, NY, USA) was used to initialize the actigraphs in zero crossing mode to collect data in 1 min epochs, while the software Action W2 (version 2.7.3285; Ambulatory Monitoring Inc., Ardsley, NY, USA) and Action 4 (version 1.16; Ambulatory Monitoring Inc., Ardsley, NY, USA) were used to score sleep and extract raw motor activity, respectively.

Tonetti L., Occhionero M., Giovagnoli S., Giudetti F., Briganti E, & Natale V. (2024). Activity-Based Prospective Memory and Motor Sleep Inertia in Insomnia. Brain Sciences, 14(12), 1248.

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Actigraphy Sleep Measurement in Twins

2023

Parent and twin actigraphy sleep were measured using the Micro Motion Logger Watch, a wrist-based accelerometer worn on participants’ non-dominant wrists (Ambulatory Monitoring, Inc. Ardsley, NY USA). Activity was measured in 1-minute epochs using a zero-crossing mode; periods of sleep and waking were detected using the Sadeh algorithm in Action W-2 (Version 2.7.1; Ambulatory Monitoring). Actigraphy has been validated against polysomnography^{23 (link)} and demonstrates good reliability when assessed 4–5 nights or more.^{24 (link)} Four sleep parameters were examined in the present study: (1) sleep duration (i.e., total time asleep (in hours), excluding waking episodes), (2) sleep efficiency (i.e., ratio of time spent asleep (duration) to total time in bed, with total time in bed consisting of true sleep and waking episodes), (3) sleep midpoint (i.e., midpoint between sleep onset and offset), (4) sleep latency (i.e., number of minutes to sleep onset from first attempting to fall asleep).
Compliance was high, of twins who participated in actigraphy (n = 612), 42 (6.9%) children had watches that malfunctioned upon data download, 5 (0.8%) watches were lost, 44 (7.2%) had insufficient data to analyze (0–2 nights of sleep) or decided opt out of the actigraphy portion of the study, and 1 (0.2%) participant wore the watch but was removed from analyses due to developmental disability that may affect sleep. Of the 516 children with valid actigraphy data, 76.0% (n = 393) of children had 7 or more nights of data, 13.7% (n=71) had 6 nights of data, 5.0% (n = 26) had 5 nights of data, 2.7% (n = 14) had 4 nights of data, and 2.5% (n = 13) had 3 nights of data. Of the primary caregivers who participated in actigraphy (n = 290), 24 (8.2%) had watches that malfunctioned upon download, 20 (6.9%) had less than 3 nights of sleep or did not wear the watch, and 1 had a watch that was lost (0.3%). Of the 245 primary caregivers with 3 or more nights of actigraphy data, 76.7% (n = 188) had 7 or more nights of data, 14.3% (n = 35) had 6 nights, 5.7% (n = 14) had 5 nights, 2.9% (n = 7) had 4 nights, and 0.4% (n = 1) had 3 nights of data. The valid actigraphy data used in the current study came from 287 different families. Of these, 71.8% (N = 206) had data from all three family members (i.e., both twins and primary caregiver), 21.6% (N = 62) had sleep data from two family members (n = 28 had data for both twins but not the primary caregiver, n = 34 had data for the primary caregiver and one twin), and 6.6% (N = 19) had sleep data from one family member (n = 14 had data for one twin, n = 5 had data for primary caregiver).

Cao J., Ihee H, & Zewail A.H. (1999). Ultrafast electron diffraction and direct observation of transient structures in a chemical reaction. Proceedings of the National Academy of Sciences of the United States of America, 96(2), 338-342.

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