Ax3 triaxial accelerometer

Manufactured by Axivity

Sourced in United Kingdom

The Axivity AX3 is a triaxial accelerometer that measures acceleration along three orthogonal axes. It is a compact, lightweight device designed for data collection and analysis. The AX3 records motion data that can be used for various applications, including activity monitoring, gait analysis, and biomechanical research.

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Lab products found in correlation

Double sided adhesive tape, by 3M (1 mentions) Opsite flexifix, by Smith & Nephew (1 mentions)

10 protocols using ax3 triaxial accelerometer

Accelerometer-based Physical Activity Assessment

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Physical activity will be assessed by Axivity AX3 triaxial accelerometers (https://axivity.com/product/ax3). At school, the research team will provide the children with accelerometers and instruct them to wear the device at the thigh using two adhesive patch 24 h/day for seven consecutive days at baseline and 6-month follow-up [31 (link)]. Based on the three-axis accelerometry data, time spent within physical activity intensity domains (sedentary, light, moderate, and vigorous) and distinct activity types (sitting or lying, moving, standing, biking, running, and walking) will be measured. These latter activity types can be determined with very high accuracy in children [32 (link)].
In addition to the physical activity measurement, the Axivity AX3 triaxial accelerometers will be used to assess children’s sleep.
Compliance to the Axivity AX3 triaxial accelerometers, measurement will be assessed as the proportion of children with at least three valid weekdays and at least one valid weekend day at baseline and follow-up. A valid day will be defined as a measurement day with less than 10% non-wear time during leisure time. Non-wear will be identified as described in Rasmussen et al. (2020) [33 (link)].
The OmGui software (https://github.com/digitalinteraction/openmovement/wiki/AX3-GUI) will be used to prepare the accelerometers and download data after the measurement periods.

Sørensen S.O., Larsen K.T., Høy T.V., Hansen A.B., Jago R., Kristensen P.L., Toftager M., Grøntved A, & Gejl A.K. (2024). Study protocol for the Screen-Free Time with Friends Feasibility Trial. Pilot and Feasibility Studies, 10, 33.

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Measuring Adolescent Physical Activity

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To measure (prolonged) sitting time and total volume of physical activity (as a confounding factor), we used the Axivity AX3 triaxial accelerometer (Axivity, Newcaste, UK), a small waterproof device (dimensions: 23 × 32.5 × 7.6 mm; 11 g). Accelerometers were initialised to measure data at 25 Hz with 8G bandwidth. Initialising and downloading was executed via OmGui (version 1.0.0.43, GitHub, Inc., 2020). Adolescents were asked to continuously wear the device for one school week (4 or 5 school days), as the focus of the initial intervention study was on school day behaviour. Adolescents were asked to keep a diary on the time periods during which the device was not worn, if any. These periods of non-wear time were specified by noting the date and time of removal and the date and time of reattachment of the device. Devices were distributed at school on Monday or Tuesday and were collected again on Friday afternoon. The Axivity device was attached to the anterior mid-line of the adolescents’ right thigh using 3MTM Tegaderm Transparent Film Roll (3M, St. Paul, MN, USA). The device was oriented with the x-axis vertically (while standing) and the USB port facing down, y-axis horizontally to the left, and the z-axis horizontally forward.

Verloigne M., Van Oeckel V., Brondeel R, & Poppe L. (2021). Bidirectional associations between sedentary time and sleep duration among 12- to 14-year-old adolescents. BMC Public Health, 21, 1673.

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Accelerometry Monitoring Protocol for Posture

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Each participant had an Axivity AX3 triaxial accelerometer (Axivity, Newcastle, UK) tape-mounted to the skin of the lower back. Accelerometers were initialized to measure raw accelerometry and temperature data at 30 Hz with ±8G bandwidth and data stored in the binary .gt3x ActiLife file format. The accelerometer was placed above the upper point of the posterior iliac crest to the right side of the spine with the positive x-axis pointing downward and negative z-axis pointing forward. The accelerometer was tape-mounted using a four step protocol. First, the skin was cleaned using an alcohol wipe. Second, a 3*5 cm piece of Fixomull tape (BSN Medical) with a 1*2 cm piece of double sided adhesive tape (3 M, Hair-set) on top was placed on the skin. Third, the accelerometer was attached to the double sided adhesive tape. Fourth, an 8*10 cm piece of Opsite Flexifix (Smith & Nephew) with rounded corners was placed on top. The participants were instructed to wear the accelerometer at all times, including sleep and water activities, and not to put the accelerometer back on if it detached before the planned end of measurements.

Schneller M.B., Duncan S., Schipperijn J., Nielsen G., Mygind E, & Bentsen P. (2017). Are children participating in a quasi-experimental education outside the classroom intervention more physically active?. BMC Public Health, 17, 523.

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Accelerometer Monitoring Protocol for Functional Capacity

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In all cohorts, an accelerometer was taped on the anterior aspect of the dominant thigh using transparent adhesive film before the start of the functional capacity measurement. Participants were asked to wear the accelerometer for 7 d in the LEUVEN and AGNES studies and 4 d in the FIREA study, and then return the device to the laboratory. LEUVEN and AGNES used the UKK RM42 triaxial accelerometer (sampling continuously at 100 Hz, 13-bit analog-to-digital conversion, ±16g acceleration range; UKK Terveyspalvelut Oy, Tampere, Finland), and the FIREA study used the Axivity AX3 triaxial accelerometer (sampling continuously at 100 Hz, 13-bit analog-to-digital conversion, ±8g acceleration range; Axivity Ltd, Newcastle, UK).

LÖPPÖNEN A., DELECLUSE C., SUORSA K., KARAVIRTA L., LESKINEN T., MEULEMANS L., PORTEGIJS E., FINNI T., RANTANEN T., STENHOLM S., RANTALAINEN T, & VAN ROIE E. (2023). Association of Sit-to-Stand Capacity and Free-Living Performance Using Thigh-Worn Accelerometers among 60- to 90-Yr-Old Adults. Medicine and Science in Sports and Exercise, 55(9), 1525-1532.

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Objective Measurement of Physical Activity and Sleep

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Appropriate written consent was obtained from 103,712 participants studied between 2013 and 2015 (participants are now aged 43–79 years) to invite them to wear a wrist-worn accelerometer (Axivity AX3 triaxial accelerometer) continuously for 7 consecutive days on their dominant wrist. This allowed objectively measured physical activity and sleep/wake pattern data to be collected.24 ,25 (link)

Zhu G., Cassidy S., Hiden H., Woodman S., Trenell M., Gunn D.A., Catt M., Birch-Machin M, & Anderson K.N. (2021). Exploration of Sleep as a Specific Risk Factor for Poor Metabolic and Mental Health: A UK Biobank Study of 84,404 Participants. Nature and Science of Sleep, 13, 1903-1912.

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UK Biobank Accelerometer Data Protocol

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We used the UK Biobank (application # 79654), a population-based cohort of over 500,000 individuals from England, Scotland, and Wales aged 40-69 at recruitment between 2006 and 2010. Follow-up time was censored at March 31 st , 2016 in Wales, September 30 th , 2021 in England, and July 31 st , 2021 in Scotland. The data were accessed most recently on September 4, 2023, and the authors did not have access to personally identifiable information. This dataset contains information on genetics, health behaviors, socioeconomic status, and health status and is described in detail elsewhere [28] . Between 2013 and 2015, participants with an email address were invited except those in the North West region due to concerns about participant burden. Out of 236,519 invitees, a subsample of 103,712 individuals responded to an email recruiting them to wear a wrist-worn Axivity AX3 triaxial accelerometer continuously for seven days on their dominant wrist and provided data. We applied exclusion criteria used previously in this dataset and dropped participants who failed calibration through either insufficient or unreliable data, had implausibly high overall acceleration averages, had wear time under three days, or did not have 24 unique hours of wear in a 24-hour cycle [29, 30] .

Schell R.C., Dow W.H., Fernald L.C.H., Bradshaw P.T, & Rehkopf D.H. (2024). Joint association of genetic risk and accelerometer-measured physical activity with incident coronary artery disease in the UK biobank cohort. PloS one, 19(6).

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Objective Assessment of Physical Activity and Sleep Patterns in the UK Biobank

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A cross-sectional analysis was conducted on baseline data and objectively assessed accelerometry data from the UK Biobank. The UK Biobank recruited approximately 500,000 participants aged 40–69 within the general population of the UK. Full-scale recruitment took place between 2007 and 2010[22 ]. Participants were invited to a baseline assessment visit where physical measurements and biological samples were collected. Sociodemographic, occupation, health-related and lifestyle information were collected through the use of touch-screen questionnaires which contains approximately 314 questions. To measure self-reported sleep duration, participants were asked “About how many hours sleep do you get in every 24 hours? (Please include naps)”. Work patterns including shift work and night shift was asked about but sleep diary data was not collected[22 ].
After appropriate written consent, 103,712 participants from the UK Biobank study were later invited to wear wrist acceleration sensors (Axivity AX3 triaxial accelerometer) on their dominant wrist continuously for 7 consecutive days. Recruitment occurred between 2013 and 2015 (participants aged between 43 and 79). This allowed objective measurements of their physical activity and sleep-wake patterns[22 ,23 (link)].

Zhu G., Catt M., Cassidy S., Birch-Machin M., Trenell M., Hiden H., Woodman S, & Anderson K.N. (2019). Objective sleep assessment in >80,000 UK mid-life adults: Associations with sociodemographic characteristics, physical activity and caffeine. PLoS ONE, 14(12), e0226220.

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Validating Intermittent Fatigue Test with MMG

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Two experiments were performed to provide evidence for the validity of using an
intermittent fatigue test paired with MMG, measured as skin surface acceleration, to
detect functional changes in the muscle. During the first experiment, acceleration data
was collected on 6 subjects using an Axivity AX3 triaxial accelerometer (Axivity:
Newcastle, UK) and was correlated to torque measurements recorded simultaneously using a
Cybex Humac Norm Dynamometer (Cybex: Bayreuth, Germany). The second experiment was
performed to determine whether the MMG data collected during the proposed fatigue test
would detect reduced muscle function when it should be present. A separate set of subjects
(n = 10) was recruited for this second experiment. Subjects performed
two fatigue tests (control vs tourniquet) each for both the ankle dorsiflexors (AD) and
the wrist extensors (WE) as described below. Tourniquet trials were assigned in a
counterbalanced design, with half the subjects performing the tourniquet trials first and
the other half performing them during the second trial. Subjects had a 10-min break
between trials to allow the muscle time to recover.

Brandenberger K.J., Rawdon C.L., Armstrong E., Lonowski J, & Cooper L. (2023). A non-volitional skeletal muscle endurance test measures functional changes associated with impaired blood flow. Journal of Rehabilitation and Assistive Technologies Engineering, 10, 20556683231164339.

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UK Biobank Accelerometer Study

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Between 2013 and 2015, 103 712 participants enrolled in the UKB accepted the invitation to wear an accelerometer for 7 days and provided accelerometer data. The participants who accepted wore an Axivity AX3 triaxial accelerometer (https://axivity.com/product/ax3) on their dominant wrist and continued with normal daily activities. Over 7 days, participants’ activity was recorded by the accelerometer. The accelerometer data were pre-processed by the UKB accelerometer expert working group.¹⁵ (link) Details of data collection and pre-processing can be found at http://biobank.ctsu.ox.ac.uk/crystal/refer.cgi?id=131600 (accessed 29 September 2021).

Nikbakhtian S., Reed A.B., Obika B.D., Morelli D., Cunningham A.C., Aral M, & Plans D. (2021). Accelerometer-derived sleep onset timing and cardiovascular disease incidence: a UK Biobank cohort study. European Heart Journal. Digital Health, 2(4), 658-666.

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UK Biobank Actigraphy Quality Control

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The study sample were participants who took part in the UK Biobank study. Initially, over 500,000 participants in the UK National Health Service registry were recruited as part of this study between 2006 and 2010 (UKB handbook). A subset of these participants wore wrist activity monitors (AX3 triaxial accelerometer, Axivity, Newcastle upon Tyne, UK) for 7 days, and inclusion in this current study was restricted its analysis to participants with actigraphy data that passed quality control and contained sufficient weekend day and weekday data to allow for analysis of sleep offset differences; data pre-processing was conducted by the UK Biobank Accelerometer Expert Working Group, with details available at http://biobank.ctsu.ox.ac.uk/crystal/docs/PhysicalActivityMonitor.pdf (accessed on 1 August 2022). Further details on the quality control applied to the actigraphy data can be found in [31 (link)]. This study was covered by the generic ethical approval for UK Biobank studies from the NHS National Research Ethics Service (approval letter dated 17 June 2011, Ref 11/NW/0382) for project #26209. All participants provided their full informed consent to participate in the UK Biobank and to have their data analyzed for all extending research. This research was further approved by the Maynooth University Research Ethics Committee.

Kelly R.M., McDermott J.H, & Coogan A.N. (2022). Differences in Sleep Offset Timing between Weekdays and Weekends in 79,161 Adult Participants in the UK Biobank. Clocks & Sleep, 4(4), 658-674.

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