Comparison of Outcomes Derived from the ActiGraph GT3X+ and the Axivity AX3 Accelerometer to Objectively Measure 24-Hour Movement Behaviors in Adults: A Cross-Sectional Study

Craemer, Marieke De; Verbestel, Vera

doi:10.3390/ijerph19010271

Cited by 15 publications

(10 citation statements)

References 19 publications

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“…PA was measured by Axivity AX3 (Axivity AX3, Axivity Ltd., Newcastle upon Tyne, UK), which is a waterproof (to 1.5 m) triaxial accelerometer, equipped with temperature sensors, capturing acceleration along three orthogonal axes, can be positioned at various body locations [5], and considered feasible and practical for PA measures [5,10]. The AX3 were set to capture acceleration at a sample rate of 100 Hz and a range of ±8 g (where 1 g is equivalent to Earth's gravity) [21].…”

Section: Accelerometers and Pedometermentioning

confidence: 99%

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Validation of Oura ring energy expenditure and steps in laboratory and free-living

Kristiansson

Fridolfsson

Arvidsson

et al. 2023

BMC Med Res Methodol

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Background Commercial activity trackers are increasingly used in research and compared with research-based accelerometers are often less intrusive, cheaper, with improved storage and battery capacity, although typically less validated. The present study aimed to determine the validity of Oura Ring step-count and energy expenditure (EE) in both laboratory and free-living. Methods Oura Ring EE was compared against indirect calorimetry in the laboratory, followed by a 14-day free-living study with 32 participants wearing an Oura Ring and reference monitors (three accelerometers positioned at hip, thigh, and wrist, and pedometer) to evaluate Oura EE variables and step count. Results Strong correlations were shown for Oura versus indirect calorimetry in the laboratory (r = 0.93), and versus reference monitors for all variables in free-living (r ≥ 0.76). Significant (p < 0.05) mean differences for Oura versus reference methods were found for laboratory measured sitting (− 0.12 ± 0.28 MET), standing (− 0.27 ± 0.33 MET), fast walk (− 0.82 ± 1.92 MET) and very fast run (− 3.49 ± 3.94 MET), and for free-living step-count (2124 ± 4256 steps) and EE variables (MET: − 0.34-0.26; TEE: 362–494 kcal; AEE: − 487-259 kcal). In the laboratory, Oura tended to underestimate EE with increasing discrepancy as intensity increased. The combined activities and slow running in the laboratory, and all MET placements, TEE hip and wrist, and step count in free-living had acceptable measurement errors (< 10% MAPE), whereas the remaining free-living variables showed close to (≤13.2%) acceptable limits. Conclusion This is the first study investigating the validity of Oura Ring EE against gold standard methods. Oura successfully identified major changes between activities and/or intensities but was less responsive to detailed deviations within activities. In free-living, Oura step-count and EE variables tightly correlated with reference monitors, though with systemic over- or underestimations indicating somewhat low intra-individual validity of the ring versus the reference monitors. However, the correlations between the devices were high, suggesting that the Oura can detect differences at group-level for active and total energy expenditure, as well as step count.

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Section: Accelerometers and Pedometermentioning

confidence: 99%

“…steps, activity intensities, energy expenditure (EE)), and sleep (e.g. sleep stages and duration) [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Validation of Oura ring energy expenditure and steps in laboratory and free-living

Kristiansson

Fridolfsson

Arvidsson

et al. 2023

BMC Med Res Methodol

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“…The systematic review by Godhino et al [ 92 ] highlighted that the AX3 could reliably and accurately measure 3D accelerations from the lumbar region and could be used to support measurement in interventional assessments, as well as disease classification and grading. It was also found that users found the AX3 more practical to wear for longer periods compared with other wearable devices, and the positive user experience may have improved participant compliance [ 96 ]. These characteristics could explain why the Axivity AX3 was commonly used in the included publications, and it provided reliable and accurate measurements when conducting physical assessments while being comfortable and practical for use in a hospital setting.…”

Section: Discussionmentioning

confidence: 99%

An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review

Tam,

Alajlani,

Abd-alrazaq

2023

J Med Internet Res

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Background The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom. Objective In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom. Methods A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors. Results Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis. Conclusions This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.

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“…The Axivity AX3 is a valid and reliable method for the measurement of physical activity and sedentary behaviour. 22 …”

Section: Methods and Analysismentioning

confidence: 99%

Digital home monitoring for capturing daily fluctuation of symptoms; a longitudinal repeated measures study: Long Covid Multi-disciplinary Consortium to Optimise Treatments and Services across the NHS (a LOCOMOTION study)

Mansoubi,

Dawes,

Bhatia

et al. 2023

BMJ Open

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IntroductionA substantial proportion of COVID-19 survivors continue to have symptoms more than 3 months after infection, especially of those who required medical intervention. Lasting symptoms are wide-ranging, and presentation varies between individuals and fluctuates within an individual. Improved understanding of undulation in symptoms and triggers may improve efficacy of healthcare providers and enable individuals to better self-manage their Long Covid. We present a protocol where we aim to develop and examine the feasibility and usability of digital home monitoring for capturing daily fluctuation of symptoms in individuals with Long Covid and provide data to facilitate a personalised approach to the classification and management of Long Covid symptoms.Methods and analysisThis study is a longitudinal prospective cohort study of adults with Long Covid accessing 10 National Health Service (NHS) rehabilitation services in the UK. We aim to recruit 400 people from participating NHS sites. At referral to study, 6 weeks and 12 weeks, participants will complete demographic data (referral to study) and clinical outcome measures, including ecological momentary assessment (EMA) using personal mobile devices. EMA items are adapted from the COVID-19 Yorkshire Rehabilitation Scale items and include self-reported activities, symptoms and psychological factors. Passive activity data will be collected through wrist-worn sensors. We will use latent class growth models to identify trajectories of experience, potential phenotypes defined by co-occurrence of symptoms and inter-relationships between stressors, symptoms and participation in daily activities. We anticipate that n=300 participants provide 80% power to detect a 20% improvement in fatigue over 12 weeks in one class of patients relative to another.Ethics and disseminationThe study was approved by the Yorkshire & The Humber—Bradford Leeds Research Ethics Committee (ref: 21/YH/0276). Findings will be disseminated in peer-reviewed publications and presented at conferences.Trial registration numberISRCTN15022307.

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Comparison of Outcomes Derived from the ActiGraph GT3X+ and the Axivity AX3 Accelerometer to Objectively Measure 24-Hour Movement Behaviors in Adults: A Cross-Sectional Study

Cited by 15 publications

References 19 publications

Validation of Oura ring energy expenditure and steps in laboratory and free-living

Validation of Oura ring energy expenditure and steps in laboratory and free-living

An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review

Digital home monitoring for capturing daily fluctuation of symptoms; a longitudinal repeated measures study: Long Covid Multi-disciplinary Consortium to Optimise Treatments and Services across the NHS (a LOCOMOTION study)

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