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Axivity Ltd axivity accelerometer data
Axivity Accelerometer Data, supplied by Axivity Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/accelerometer+data/accelerometer/pm40911648-111-1-1
Average 86 stars, based on 1 article reviews
axivity accelerometer data - by Bioz Stars, 2026-09
86/100 stars

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Activity Assay:

Article Title: Accelerometer-based physical activity, air pollution and risk of dementia subtypes: a prospective study using UK biobank
Article Snippet: .. UK Biobank participants were invited via email to wear a wrist-worn Axivity A × 3 triaxial accelerometer to capture their physical activity level for a 7-day period. ..

Article Title: Endurance exercise intervention reduces vegetable intake which compromises the achievement of glycemic benefit: a randomized controlled, three-period crossover trial.
Article Snippet: .. Monitoring of physical activity was performed in a substudy of 103 666 participants who responded to wear an Axivity AX3 wrist-worn accelerometer for 7 days, and in the end, acceptable data were obtained from 96 644 participants after excluding those with insufficient wear time, poor quality of calibration, and those with an abnormal average acceleration more than 100 milligravities (mg) [36]. ..

Article Title: Monitoring and evaluation of digital mental health assessments in adults with mild to moderate intellectual disability and concurrent mental illness: a pilot study.
Article Snippet: .. Data collection focused on the aspects of the feasibility of capturing mood, anxiety, sleep, and physical activity data using digital tools within routine clinical settings, Identification of participants attending psychiatric clinicwith mild to moderate learning disability (n=104) Unknown reasons for non-participation (n=88) Telephone/email screening for eligibility (n=16) Negative Response (n=4) Home visit for consent (n=12) Did not consent (n=2) Total participants that consented (n=10) Total participants that receivedthe Axivity accelerometer (n=10) Id en tif ic at io n Sc re en in g El ig ib ili ty Paper-basedrecordingof mood and anxiety (n=10) Mobile app recordingof mood and anxiety (n=10) Wore Axivity accelerometer for 7 days (n=10) Da ta C ol le ct io n AR TIC LE IN PR ES S Page of rather than on a comprehensive validation of measurement accuracy. ..

other:

Article Title: Lifestyle trajectories of boys and girls during the transition to secondary school in British Columbia, Canada, before and during the COVID-19 pandemic
Article Snippet: For the non-pandemic cohort, children’s grade 7 data were collected in schools, where they completed a 45-minute online questionnaire on iPads and were fitted with an Axivity AX3 wrist-worn accelerometer.

Article Title: Are different consumer sleep technologies measuring the same essential aspects of sleep?
Article Snippet: In , using data from all devices (with Sleep Diary information shown as inverted triangles and a solid navy-blue line), it can be seen that the participant had a consistent bedtime and this is accurately captured by the contactless under-mattress device (WSA), while the wearable accelerometer (Axivity) and smartwatch (WW) show larger differences in their recording times.

Article Title: Lifestyle trajectories of boys and girls during the transition to secondary school in British Columbia, Canada, before and during the COVID-19 pandemic.
Article Snippet: For the non- pandemic cohort, children’s grade 7 data were collected in schools, where they completed a 45-minute online questionnaire on iPads and were fitted with an Axivity AX3 wrist-worn accelerometer.

Article Title: Daily steps are a predictor of, but perhaps not a risk factor for Parkinson’s disease: findings from the UK Biobank
Article Snippet: A subgroup of participants consented to wear an Axivity AX3 wrist-worn accelerometer for up to 7 days between 2013 and 2015 .

Biomarker Discovery:

Article Title: Monitoring and evaluation of digital mental health assessments in adults with mild to moderate intellectual disability and concurrent mental illness: a pilot study.
Article Snippet: .. Data collection focused on the aspects of the feasibility of capturing mood, anxiety, sleep, and physical activity data using digital tools within routine clinical settings, Identification of participants attending psychiatric clinicwith mild to moderate learning disability (n=104) Unknown reasons for non-participation (n=88) Telephone/email screening for eligibility (n=16) Negative Response (n=4) Home visit for consent (n=12) Did not consent (n=2) Total participants that consented (n=10) Total participants that receivedthe Axivity accelerometer (n=10) Id en tif ic at io n Sc re en in g El ig ib ili ty Paper-basedrecordingof mood and anxiety (n=10) Mobile app recordingof mood and anxiety (n=10) Wore Axivity accelerometer for 7 days (n=10) Da ta C ol le ct io n AR TIC LE IN PR ES S Page of rather than on a comprehensive validation of measurement accuracy. ..



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Devices used in the study: A wearable camera was used to provide visual confirmation of human behavior performed in the wild. We utilized the smartwatch tri-axial accelerometer and gyroscope to estimate energy expenditure using our proposed algorithm and compared it against multiple algorithms , , , , – using the hip-worn ActiGraph.

Journal: Scientific Reports

Article Title: Developing and comparing a new BMI inclusive energy expenditure algorithm on wrist-worn wearables

doi: 10.1038/s41598-025-99963-0

Figure Lengend Snippet: Devices used in the study: A wearable camera was used to provide visual confirmation of human behavior performed in the wild. We utilized the smartwatch tri-axial accelerometer and gyroscope to estimate energy expenditure using our proposed algorithm and compared it against multiple algorithms , , , , – using the hip-worn ActiGraph.

Article Snippet: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K_m$$\end{document} motion estimates of accelerometer data segments are calculated by the Panasonic equation and aggregated based on the selected window size (Eq.).

Techniques: