Adequacy of physical activity time evaluated by a triaxial accelerometer

Sato, Taku; Kitamura, Natsuki; Kawagoshi, Atsuyoshi; Sakata, Shunichi; Kiyokawa, Noritaka; Sugawra, Keiyu; Takahashi, Hitomi; Satake, Masahiro; Shioya, Takanobu

doi:10.3951/sobim.35.197

Cited by 4 publications

(4 citation statements)

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“…The system can also measure the frequency of postural changes, ie, getting up (bodily change from a recumbent to an upright position) and standing up (bodily change from a sitting to an upright position), as shown in Figure 2. Moreover, the A-MES can measure walking time and divide this into slow (<2 km/hour) and fast (≥2 km/hour) walking time by changing the sensor threshold 18. We studied validation of the A-MES in part of the population included in this study.…”

Section: Methodsmentioning

confidence: 99%

“…Each subject wore an A-MES during the protocol, and a video recording was made of the entire protocol. The time spent performing the various activities and postures was obtained by chronometer analysis of the video recording and used as the criterion standard 1. The Pearson’s product–moment correlation coefficient was used for correlations between total time and maximum continuously active time measured by A-MES and video recording.…”

Section: Supplementary Materialsmentioning

confidence: 99%

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Quantitative assessment of walking time and postural change in patients with COPD using a new triaxial accelerometer system

Kawagoshi¹,

Kiyokawa²,

Sugawara³

et al. 2013

COPD

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BackgroundThe purpose of this study was to quantify the walking time and frequency of postural changes in daily life in patients with chronic obstructive pulmonary disease (COPD) using a new triaxial accelerometer system.MethodsTwenty-six elderly patients with stable COPD (age 76.8 ± 6.2 years; percent forced expiratory volume in one second [%FEV1] 52.9% ± 26.3%) and 20 age-matched elderly subjects (age 73.0 ± 4.2 years; %FEV1 124.0% ± 22.3%) participated in the study. The subjects’ time spent walking (slow, fast), standing, sitting, and lying down and the frequency of their postural changes (getting up, standing up) were assessed for 7 consecutive days using an Activity Monitoring And Evaluation System (A-MES™). We analyzed the relationships among walking times, frequency of postural changes, and physiologic factors in both COPD patients and controls.ResultsThe COPD patients’ total walking time, including slow (<2 km/hour) and fast (≥2 km/hour) walking, and their frequency of standing up were significantly lower than those of the age-matched controls (P < 0.01). The fast walking time in daily life was significantly correlated with the 6-minute walking distance, quadriceps femoris muscle force, and dyspnea (P < 0.01).ConclusionThese results suggest that both slow (<2 km/hour) and fast (≥2 km/hour) walking time and frequency of postural changes is significantly decreased in COPD patients compared with healthy elderly subjects. The data also suggest that the COPD patients’ different walking times in daily life are significantly correlated with exercise capacity and dyspnea. The 6-minute walking distance had the strongest correlation with fast walking time.

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Section: Methodsmentioning

confidence: 99%

Section: Supplementary Materialsmentioning

confidence: 99%

Quantitative assessment of walking time and postural change in patients with COPD using a new triaxial accelerometer system

Kawagoshi¹,

Kiyokawa²,

Sugawara³

et al. 2013

COPD

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“…Smartphone sensors can collect a variety of data. We collected data from accelerometers and gyroscopes, which are the most commonly used sensors for analyzing body motion (e.g., [17,18]).…”

Section: Study Settingmentioning

confidence: 99%

Effects of chest movements while sitting on Navon task performance and stress levels

Arima

2023

BMC Digit Health

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Background This study explored physical activity during remote work, most of which takes place while sitting in front of a computer. The purpose of Experiment 1 was to develop a classification for body motion by creating a neural net that can distinguish among several kinds of chest movement. Experiment 2 examined the effects of chest movements on stress and performance on the Navon test to validate the model developed in Experiment 1. Method and results The procedures for this study were as follows. Experiment 1: Creation of the body movement classification model and preliminary experiment for Experiment 2. Data from five participants were used to construct a machine-learning categorization model. The other three participants participated in a pilot study for Experiment 2. Experiment 2: Model validation and confirmation of stress measurement validity. We recruited 34 new participants to test the validity of the model developed in Experiment 1. We asked 10 of the 34 participants to retake the stress measurement since the results of the stress assessment were unreliable. Using LSTM models, we classified six categories of chest movement in Experiment 1: walking, standing up and sitting down, sitting still, rotating, swinging, and rocking. The LSTM models yielded an accuracy rate of 83.8%. Experiment 2 tested the LSTM model and found that Navon task performance correlated with swinging chest movement. Due to the limited reliability of the stress measurement results, we were unable to draw a conclusion regarding the effects of body movements on stress. In terms of cognitive performance, swinging of the chest reduced RT and increased accuracy on the Navon task (β = .015 [-.003,.054], R2 = .31). Conclusions LSTM classification successfully distinguished subtle movements of the chest; however, only swinging was related to cognitive performance. Chest movements reduced the reaction time, improving cognitive performance. However, the stress measurements were not stable; thus, we were unable to draw a clear conclusion about the relationship between body movement and stress. The results indicated that swinging of the chest improved reaction times in the Navon task, while sitting still was not related to cognitive performance or stress. The present article discusses how to collect sensor data and analyze it using machine-learning methods as well as the future applicability of measuring physical activity during remote work.

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“…In addition, it is critical to discriminate between slow and fast walking speeds since walking is the most basic form of physical activity, and the level of physical activity differs according to walking speed. We recently reported our development of a new triaxial accelerometer system for the assessment of daily physical activity, which measures daily walking, standing, sitting and lying times [9][10][11]. Walking speed is a more objective measure for the assessment of physical activity, and dividing the calculated walking time by walking speed could enable more precise assessments of physical activity.…”

Section: Original Articlementioning

confidence: 99%

Evaluation of walking time according to walking speed using a triaxial accelerometer system

Miura

Satake

Tamura

et al. 2013

Jpn J Compr Rehabil Sci

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T. Evaluation of walking time according to walking speed using a triaxial accelerometer system. Jpn J Compr Rehabil Sci 2013; 4: 73−79. Purpose: The primary purpose of this study was to quantify the walking time in daily life as slow and fast walking times using a newly improved triaxial accelerometer system. The secondary purpose was to study the slow and fast walking times of healthy young and elderly adults and stable patients with chronic obstructive pulmonary disease (COPD) using this system. Methods: Twenty-six healthy young subjects, 15 healthy elderly adults, and 11 stable COPD patients participated in this study. The subjects' time spent walking, standing, sitting, and lying down were assessed using an activity monitoring and evaluation system (A-MES TM ). We evaluated the slow and fast walking times according to changes in the detection threshold in the system's software. Results: The distinguishing speed was found to be 2 km/h, and thus we divided the subjects' walking time into slow (<2 km/h) and fast (≥2 km/h) walking using 2 km/h to distinguish the speed. Ninety-five percent of the walking measured by the A-MES TM was slow walking. The fast walking time of the stable COPD patients was significantly shorter than that of the healthy young and elderly adults (p<0.01). Conclusion: These results suggest that walking time in daily life can be differentiated as slow (<2 km/h) and fast (≥2 km/h) walking using a new triaxial accelerometer system.

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Adequacy of physical activity time evaluated by a triaxial accelerometer

Cited by 4 publications

References 4 publications

Quantitative assessment of walking time and postural change in patients with COPD using a new triaxial accelerometer system

Quantitative assessment of walking time and postural change in patients with COPD using a new triaxial accelerometer system

Effects of chest movements while sitting on Navon task performance and stress levels

Evaluation of walking time according to walking speed using a triaxial accelerometer system

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