Using accelerometry to classify physical activity intensity in older adults: What is the optimal wear‐site?

Duncan, Michael; Rowlands, Alex V.; Lawson, Chelsey; Wright, Sheila Leddington; Hill, Matthew; Morris, Martyn G.; Eyre, Emma; Tallis, Jason

doi:10.1080/17461391.2019.1694078

Cited by 13 publications

(22 citation statements)

References 54 publications

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“…When the study focus is on sedentary behaviour, thigh placement is currently considered the gold standard 90 . In populations with decreased functional capacity, ankle placement is also gaining popularity as it performs well even with slow gait where other placements usually fail 79,91 . In a review by Clark et al ., the authors advocated that there is no ‘one site fits all’ approach to the selection of accelerometer site or analytical technique, and that study design and focus should always inform the most suitable location of attachment, and be driven by the type of activity being characterized 92 …”

Section: Discussionmentioning

confidence: 99%

“…Time spent in MVPA is a standard measure of PA, which is related to the most current PA guidelines that recommend achieving at least 150 min of MVPA weekly 1 . To calculate the time spent in MVPA, accelerometer output needs to be calibrated, usually against indirect calorimetry, to establish cut points distinguishing between various intensity levels 79 . However, different populations (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…1 To calculate the time spent in MVPA, accelerometer output needs to be calibrated, usually against indirect calorimetry, to establish cut points distinguishing between various intensity levels. 79 However, different populations (e.g. children vs. adults vs. elderly) have drastically different cut points, whilst different placements (e.g.…”

Section: Physical Activity Metricsmentioning

confidence: 99%

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Advances in accelerometry for cardiovascular patients: a systematic review with practical recommendations

et al. 2020

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Aims Accelerometers are becoming increasingly commonplace for assessing physical activity; however, their use in patients with cardiovascular diseases is relatively substandard. We aimed to systematically review the methods used for collecting and processing accelerometer data in cardiology, using the example of heart failure, and to provide practical recommendations on how to improve objective physical activity assessment in patients with cardiovascular diseases by using accelerometers. Methods and results Four electronic databases were searched up to September 2019 for observational, interventional, and validation studies using accelerometers to assess physical activity in patients with heart failure. Study and population characteristics, details of accelerometry data collection and processing, and description of physical activity metrics were extracted from the eligible studies and synthesized. To assess the quality and completeness of accelerometer reporting, the studies were scored using 12 items on data collection and processing, such as the placement of accelerometer, days of data collected, and criteria for non-wear of the accelerometer. In 60 eligible studies with 3500 patients (of those, 536 were heart failure with preserved ejection fraction patients), a wide variety of accelerometer brands (n = 27) and models (n = 46) were used, with Actigraph being the most frequent (n = 12), followed by Fitbit (n = 5). The accelerometer was usually worn on the hip (n = 32), and the most prevalent wear period was 7 days (n = 22). The median wear time required for a valid day was 600 min, and between two and five valid days was required for a patient to be included in the analysis. The most common measures of physical activity were steps (n = 20), activity counts (n = 15), and time spent in moderate-to-vigorous physical activity (n = 14). Only three studies validated accelerometers in a heart failure population, showing that their accuracy deteriorates at slower speeds. Studies failed to report between one and six (median 4) of the 12 scored items, with non-wear time criteria and valid day definition being the most underreported items. Conclusions The use of accelerometers in cardiology lacks consistency and reporting on data collection, and processing methods need to be improved. Furthermore, calculating metrics based on raw acceleration and machine learning techniques is lacking, opening the opportunity for future exploration. Therefore, we encourage researchers and clinicians to improve the quality and transparency of data collection and processing by following our proposed practical recommendations for using accelerometers in patients with cardiovascular diseases, which are outlined in the article.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Physical Activity Metricsmentioning

confidence: 99%

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Advances in accelerometry for cardiovascular patients: a systematic review with practical recommendations

et al. 2020

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“…Ideally, this will be done using wearable accelerometers. Indeed, wearables such as Actigraph (Pensacola, FL, USA) [41], GENEActiv (Cambridge, UK) [42], and Axivity (Newcastle, UK) [43] can be used to capture data related to intensity, frequency, duration, and timing of physical activity (Figure 2). It is expected that detailed drug treatment data including dose, frequency, time of administration, completion rate, dose reductions, side effects, specific treatment responses, and patient-reported outcomes should also be collected.…”

Section: Opportunities For Researchmentioning

confidence: 99%

Exercise Timing and Cancer Treatment: Avenues for Chronobiological Research

2020

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Emerging evidence suggests that cancer chronotherapy, which involves timing drug administration in accordance with a patient's circadian/ internal time, can improve treatment tolerability and efficacy. However, as cancer, its treatments, and even hospitalization can cause circadian rhythm misalignment, cancer chronotherapy may be attenuated from the start. The adjunctive therapeutic strategy 'exercise' not only improves physical function and patient-reported outcomes, but specific effects are determined by the individual's internal time and it may act as a circadian time cue. Utilizing differentially timed exercise in terms of targeting time-specific homeostatic responses to the activity, accounting for the time of peak performance, and/or targeting potential circadian system effects of exercise could thus potentiate cancer chronotherapy or cancer treatment more generally. Herein, we briefly overview cancer chronotherapy and exercise medicine in oncology, highlight potential benefit of timed exercise in cancer treatment, and discuss research opportunities to assess these potentials benefits.

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“…According to the type of gait activities to be studied, data gathered from different sensors might be required to classify them. To classify the first group (by movement speed), an accelerometer may be sufficient since it measures the change in speed with respect to time [ 4 ], even for different populations like children [ 5 ] or older adults [ 6 ]. However, when the speed is rather constant, having relatively low variability, the challenge is even greater for activities of the second group (by surface walked on).…”

Section: Introductionmentioning

confidence: 99%

Gait Activity Classification on Unbalanced Data from Inertial Sensors Using Shallow and Deep Learning

López-Nava

Valentín-Coronado

García-Constantino

et al. 2020

Sensors

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Activity recognition is one of the most active areas of research in ubiquitous computing. In particular, gait activity recognition is useful to identify various risk factors in people’s health that are directly related to their physical activity. One of the issues in activity recognition, and gait in particular, is that often datasets are unbalanced (i.e., the distribution of classes is not uniform), and due to this disparity, the models tend to categorize into the class with more instances. In the present study, two methods for classifying gait activities using accelerometer and gyroscope data from a large-scale public dataset were evaluated and compared. The gait activities in this dataset are: (i) going down an incline, (ii) going up an incline, (iii) walking on level ground, (iv) going down stairs, and (v) going up stairs. The proposed methods are based on conventional (shallow) and deep learning techniques. In addition, data were evaluated from three data treatments: original unbalanced data, sampled data, and augmented data. The latter was based on the generation of synthetic data according to segmented gait data. The best results were obtained with classifiers built with augmented data, with F-measure results of 0.812 (σ = 0.078) for the shallow learning approach, and of 0.927 (σ = 0.033) for the deep learning approach. In addition, the data augmentation strategy proposed to deal with the unbalanced problem resulted in increased classification performance using both techniques.

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Using accelerometry to classify physical activity intensity in older adults: What is the optimal wear‐site?

Abstract: This document is the author's post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Cited by 13 publications

References 54 publications

Advances in accelerometry for cardiovascular patients: a systematic review with practical recommendations

Advances in accelerometry for cardiovascular patients: a systematic review with practical recommendations

Exercise Timing and Cancer Treatment: Avenues for Chronobiological Research

Gait Activity Classification on Unbalanced Data from Inertial Sensors Using Shallow and Deep Learning

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