The largest database of trusted experimental protocols

Motionlogger Micro Watch

Manufactured by Ambulatory Monitoring
Sourced in United States

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.

Automatically generated - may contain errors

10 protocols using Motionlogger Micro Watch

1

Actigraphy-Measured Sleep Patterns in Mothers and Infants

Sleep patterns were measured from activity-based sleep monitoring (actigraphy) and daily sleep diaries. Specifically, mothers wore an actigraph (Motionlogger Micro Watch, Ambulatory Monitoring Inc.) on their non-dominant wrist and infants wore an actigraph (Motionlogger Micro Watch, Ambulatory Monitoring Inc.) on their ankle for seven consecutive 24-hour periods. Actigraphy data were recorded in 1-minute epochs (Action-W software; Ambulatory Monitoring Inc., 2002 ). Sleep duration variables were measured in minutes between scored sleep onset (sleep start time) and sleep offset (sleep end time) and subsequently computed into hours for each 24-hour period. The Action-W software was used to score the data based on a validated sleep-wake scoring algorithm for infants (Sadeh et al., 1995 (link)) and adults (Cole et al., 1992 (link)). Mothers also kept a daily sleep diary used to compute sleep duration when actigraphy data was missing due to noncompliance. As such, seven days of sleep data were scored for 80.9% (n = 38) of mothers and 89.4% (n = 42) of infants, six days of sleep data were scored for 17.0% (n = 8) of mothers and 10.6% (n = 5) of infants, and five days of sleep data were scored for 2.1% (n = 1) of mothers.
+ Open protocol
+ Expand
Objective sleep indicators were collected from each twin using wrist-based accelerometers (actigraph watches) at eight years of age (Motion Logger Micro Watch; Ambulatory Monitoring, Inc., Ardsley, NY USA). Children wore watches on their non-dominant wrist for seven nights (Mnights = 6.89, SD = .53). Actigraphy data was scored using the Action-W2 program (version 2.7.1), which includes a validated algorithm to measure sleep.43 (link) Research suggests that actigraphy is reliable when measuring five more nights of sleep, and actigraphy sleep measurement has been validated against concurrent polysomnography.43 (link) From scored actigraphy data, we assessed: 1) Nighttime sleep duration (total number of hours and minutes asleep each night excluding wake bouts), and 2) sleep efficiency (percentage of time asleep each night based on the amount of time in bed).
Study staff cross-checked objective actigraph sleep periods with parent-reported bed and wake times and daily sleep diaries to identify significant outliers and equipment malfunction. Sleep data were missing or excluded from analyses for 32 children (8.4%) primarily because they did not wear the watch or due to watch malfunction. However, most of the sample was highly compliant in wearing the actigraph watch (for details see Doane et al.22 (link)).
+ Open protocol
+ Expand
Adherence to the assigned sleep schedules was monitored throughout the protocol via wrist-worn actigraphy (Motionlogger Micro Watch; Ambulatory Monitoring, Inc., Ardsley, NY) and self-reported daily sleep diary that included nightly bed- and rise-times. Study staff reconciled the diary and actigraphic sleep data conjointly with adolescents and parents at each assessment point. This provided opportunity to clarify bed and wake-times, identify artifacts (e.g. removal of the watch), and promote adherence to subsequent conditions. Final determination of sleep-wake patterns for the present analyses was based upon actigraphy. Actigraphy data were scored using validated algorithms (Sadeh, Sharkey, & Carskadon, 1994 (link)) to obtain estimates of time of sleep onset and offset, duration of the sleep period (onset to offset) and sleep efficiency (percent of the sleep period spent asleep, recognizing the potential for periods of wakefulness between onset and offset). For this study, we adopted our previously-published definition of adherence (Beebe et al., 2013 (link); Simon et al., 2015 (link)): at least one hour less nightly sleep on average during Short Sleep than Healthy Sleep.
+ Open protocol
+ Expand
Children’s objective sleep was measured using wrist-based accelerometers (actigraph watches) (Motion Logger Micro Watch; Ambulatory Monitoring, Inc., Ardsley, NY, USA). The watches were worn on non-dominant wrists for seven nights (Mnights = 6.79). The Action-W 2 program (version 2.7.1) was used to score actigraphy data, including a validated algorithm that measured sleep (Sadeh et al., 1994 (link)). Nighttime sleep duration (total number of hours and minutes asleep each night between sleep onset and offset and waking excluding wake bouts) and sleep midpoint time (the midpoint time in between bedtime and waketime) was calculated. Trained research assistants cross-validated objective actigraph sleep periods with parent-reported bed and wake times to identify significant outliers and equipment malfunction. Sleep data were missing or excluded from analyses for 67 children (12.4%) due to children not wearing the watch during the study week or watch malfunction (e.g., watch submerged in water, broken, etc.). However, over 80% in our sample wore the actigraph watch for seven or more nights, indicating high compliance with actigraph study procedures (for more details, see Doane et al., 2019 (link)).
+ Open protocol
+ Expand
Children wore wrist-based accelerometers (Motion Logger Micro Watch; Ambulatory Monitoring, Inc, Ardsley, NY) on their nondominant wrist for 7 consecutive days and nights (M = 6.81, SD = 0.67). Motion was measured in 1-minute epochs using a zero-crossing mode (i.e., threshold crossing detection where the threshold value is set to a low level of activity and the activity count value is the number of times the activity signal crosses the zero reference point within an epoch)26 (link) and data was scored using the Sadeh algorithm in Action W-2 software version 2.7.1 program.27 (link) Actigraphy is a valid measure in middle childhood.28 (link) Sleep indicators included duration (total time asleep in hours excluding waking periods), efficiency (ratio of time spent asleep to total time in bed, with total time in bed including true sleep and waking periods), midpoint (midpoint between sleep start and end), sleep onset latency (number of minutes from first attempting to fall asleep to sleep onset), and duration variability (within-person standard deviation estimate of sleep duration, averaged across all nights of the study week). Participant compliance and missing data have been previously reported for the full sample.18
+ Open protocol
+ Expand
The following data will be collected for the purpose of the research and recruitment:

From hospital records: Name, phone number, personal identification number from the Danish Civil Registration System (CPR number), diagnosis, medication prescriptions.

From actigraphy (Motionlogger Micro Watch from Ambulatory Monitoring Inc. NY): Total sleep time, sleep onset latency, number of awakenings, wake after sleep onset.

From diaries: The use of sleep and anxiety medications, bedtime and time of getting out of bed, number of awakenings and duration, wake after sleep onset and daytime naps including lengths registered in minutes.

From six validated questionnaires: The Global PSQI Score using The Pittsburgh Sleep Quality Index (PSQI), the insomnia severity score using the Insomnia Severity Index (ISI), depressive symptoms using the Major Depression Inventory (MDI) and 6-item Hamilton Rating Scale for Depression (HAM-D6), patients’ self-reported symptoms using the Self-reported Symptom State Scales (SCL-28) and anxiety symptoms using the Beck Anxiety Index (BAI) [3 (link), 25 , 28 –30 (link)].

+ Open protocol
+ Expand
In the present study, we used the actigraph Micro Motionlogger Watch (Ambulatory Monitoring, Inc., Ardsley, NY, USA). The hardware consists of a piezoelectric accelerometer with a sensitivity ≥ 0.01 g. The sampling frequency was 10 Hz while filters were set to 2–3 Hz. We initialized the actigraphs, through the Motionlogger Watchware software (Ambulatory Monitoring, Inc., Ardsley, NY, USA), to collect data, in zero crossing mode, in 1-min epochs. The primary actigraphic output, i.e., motor activity counts, can be converted into a dichotomous variable, i.e., sleep/wakefulness, according to the algorithm, previously validated against polysomnography, by Cole and colleagues [22 ,23 (link)].
+ Open protocol
+ Expand
The actigraph model Micro Motionlogger Watch (Ambulatory Monitoring, Inc., Ardsley, NY, USA) was used. The hardware was composed of a triaxal accelerometer, with a sensitivity of 0.01 g, filters at 2–3 Hz, and a sampling frequency of 32 Hz. The actigraphs were initialized in zero crossing mode through the Motionlogger Watchware software (Ambulatory Monitoring, Inc., Ardsley, NY, USA) in order to acquire motor activity data in 1-min epochs. Motor activity data can also be transformed into dichotomous information about sleep and wake according to validated algorithms [12 ,13 (link)].
+ Open protocol
+ Expand
Participants wore a Micro Motion Logger Watch (Ambulatory Monitoring, Inc. Ardsley, NY, USA) on their non-dominant wrist for seven consecutive days and nights. Full methodological details for actigraphy in this study are included in Supplementary Materials. Actigraphy has been validated against polysomnography (Sadeh et al., 1995 (link)) and has demonstrated good reliability when measured over five nights or more (Acebo et al., 1999 (link)). The current study included multiple objective sleep outcomes: (1) sleep onset latency (SOL; i.e., minutes spent in bed before falling asleep), (2) midpoint time (i.e., midpoint between sleep onset and waking), reflecting sleep schedule, and (3) sleep duration (i.e., total sleep minutes, excluding wake periods). Objective sleep data were missing from one participant (0.5%) due to mechanical problems, one participant (0.5%) who lost the actiwatch, and six participants (3.0%) who decided not to wear the actiwatch but participated in other procedures.
+ Open protocol
+ Expand
The actigraph model Micro Motionlogger Watch (Ambulatory Monitoring, Inc., Ardsley, NY, USA) was used in the current study. The device was equipped with a triaxial accelerometer with a sensitivity of 0.01 g, while the filters and sampling frequency were set at 2–3 Hz and 32 Hz, respectively. Actigraphs were initialized through the Motionlogger Watchware software (Ambulatory Monitoring, Inc., Ardsley, NY, USA) in zero crossing mode to collect data in 1 min epochs, while actigraphic records were analyzed through the software Action W2 (Ambulatory Monitoring, Inc., Ardsley, NY, USA) using the algorithms validated by Cole and Kripke [19 ] and Cole et al. [20 (link)].
+ Open protocol
+ Expand

About PubCompare

Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.

We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.

However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.

Ready to get started?

Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required

Sign up now

Revolutionizing how scientists
search and build protocols!