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Gait & Posture Oct 2022Many activity trackers have been developed, but steps can still be inconsistent from one monitor to another.
BACKGROUND
Many activity trackers have been developed, but steps can still be inconsistent from one monitor to another.
RESEARCH QUESTION
What are the differences and associations between the steps of 13 selected consumer-based and research-grade wearable devices during 1 standardized day in a metabolic chamber and 15-day free-living trials?
METHODS
In total, 19 healthy adults between 21 and 50 years-old participated in this study. Participants were equipped with 12 accelerometer-based active trackers and one pedometer (Yamasa) in order to monitor the number of steps per day. The devices were worn on the waist (ActiGraph, Omron, Actimarker, Lifedorder, Withings, and Yamasa) or non-dominant wrist (Fitbit, Garmin, Misfit, EPSON, and Jawbone), or placed in a pocket (Omron CaloriScan, and TANITA). Participants performed structured activities over a 24 h period in a chamber (Standardized day), and steps were monitored in the same participants in free-living trials for 15 successive days using the same monitors (free-living days).
RESULTS
When the 13 monitors were ranked by their steps, waist-worn ActiGraph was located at the center (7th) of the monitors both in the Standardized (12,252 ± 598 steps/day, mean ± SD) and free-living days (9295 ± 4027 steps/day). The correlation between the accelerometer-based devices was very high (r = 0.87-0.99). However, the steps of Yamasa was significantly lower in both trials than ActiGraph. The wrist-worn accelerometers had significantly higher steps than other devices both trials (P < 0.05). The differences between ActiGraph and Actimarker or Lifecorder was less than 100 steps/day in the Standardized day, and the differences between ActiGraph and Active Style Pro was less than 100 steps/day in the free-living days. Regression equation was also performed for inter-device compatibility.
SIGNIFICANCE
Step obtained from the wrist-worn, waist-worn, and pocket-type activity trackers were significantly different from each other but still highly correlated in free-living conditions.
Topics: Adult; Humans; Young Adult; Middle Aged; Fitness Trackers; Accelerometry; Exercise; Actigraphy; Wearable Electronic Devices; Reproducibility of Results
PubMed: 36030707
DOI: 10.1016/j.gaitpost.2022.08.004 -
Journal of Physical Activity & Health Feb 2021The purposes of this study were to examine accelerometer measurement reactivity (AMR) in sedentary behavior (SB), physical activity (PA), and accelerometer wear time in...
BACKGROUND
The purposes of this study were to examine accelerometer measurement reactivity (AMR) in sedentary behavior (SB), physical activity (PA), and accelerometer wear time in 2 measurement periods and to quantify AMR as a human-related source of bias for the reproducibility of SB and PA estimates.
METHODS
In total, 136 participants (65% women, mean age = 54.6 y) received 7-day accelerometry at the baseline and after 12 months. Latent growth models were used to identify AMR. Intraclass correlations were calculated to examine the reproducibility using 2-level mixed-effects linear regression analyses.
RESULTS
Within each 7-day accelerometry assessment, the participants increased their time spent in SB (b = 2.4 min/d; b = 3.8 min/d) and reduced their time spent in light PA (b = -2.0 min/d; b = -3.2 min/d), but did not change moderate to vigorous PA. The participants reduced their wear time (b = -5.2 min/d) only at the baseline. The intraclass correlations ranged from .42 for accelerometer wear time to .74 for SB. The AMR was not identified as a source of bias in any regression model.
CONCLUSIONS
AMR may influence SB and PA estimates differentially. Although 7-day accelerometry seems to be a reproducible measure, our findings highlight accelerometer wear time as a crucial confounder in analyzing SB and PA data.
Topics: Accelerometry; Exercise; Female; Humans; Male; Middle Aged; Reproducibility of Results; Sedentary Behavior
PubMed: 33440344
DOI: 10.1123/jpah.2020-0331 -
Sensors (Basel, Switzerland) May 2024Inertial signals are the most widely used signals in human activity recognition (HAR) applications, and extensive research has been performed on developing HAR...
Inertial signals are the most widely used signals in human activity recognition (HAR) applications, and extensive research has been performed on developing HAR classifiers using accelerometer and gyroscope data. This study aimed to investigate the potential enhancement of HAR models through the fusion of biological signals with inertial signals. The classification of eight common low-, medium-, and high-intensity activities was assessed using machine learning (ML) algorithms, trained on accelerometer (ACC), blood volume pulse (BVP), and electrodermal activity (EDA) data obtained from a wrist-worn sensor. Two types of ML algorithms were employed: a random forest (RF) trained on features; and a pre-trained deep learning (DL) network (ResNet-18) trained on spectrogram images. Evaluation was conducted on both individual activities and more generalized activity groups, based on similar intensity. Results indicated that RF classifiers outperformed corresponding DL classifiers at both individual and grouped levels. However, the fusion of EDA and BVP signals with ACC data improved DL classifier performance compared to a baseline DL model with ACC-only data. The best performance was achieved by a classifier trained on a combination of ACC, EDA, and BVP images, yielding F1-scores of 69 and 87 for individual and grouped activity classifications, respectively. For DL models trained with additional biological signals, almost all individual activity classifications showed improvement (-value < 0.05). In grouped activity classifications, DL model performance was enhanced for low- and medium-intensity activities. Exploring the classification of two specific activities, ascending/descending stairs and cycling, revealed significantly improved results using a DL model trained on combined ACC, BVP, and EDA spectrogram images (-value < 0.05).
Topics: Humans; Photoplethysmography; Accelerometry; Algorithms; Male; Machine Learning; Adult; Signal Processing, Computer-Assisted; Female; Human Activities; Galvanic Skin Response; Wearable Electronic Devices; Young Adult
PubMed: 38793858
DOI: 10.3390/s24103005 -
BMC Public Health Jun 2020Gendered patterns of physical activity behaviours may help explaining health inequalities between men and women. However, evidence on such patterns in the working... (Comparative Study)
Comparative Study
BACKGROUND
Gendered patterns of physical activity behaviours may help explaining health inequalities between men and women. However, evidence on such patterns in the working population is sparse. This study aimed at documenting and comparing compositions of sitting, standing and moving at work and during leisure among male and female office workers of different age.
METHODS
Sitting (including lying), standing and moving were measured using accelerometry for, on average, four working days in 55 male and 57 female Swedish office workers. Behaviours were described in terms of time spent in four exhaustive categories: sitting in short (< 30 min) and long (≥30 min) bouts, standing, and moving. In a compositional data analysis approach, isometric log-ratios (ilr) were calculated for time sitting relative to non-sitting, time in short relative to long sitting bouts, and time in standing relative to moving. Differences between genders (men vs. women), domains (work vs. leisure), and according to age were examined for each ilr using ANOVA.
RESULTS
At work, time spent sitting in short bouts, sitting in long bouts, standing, and moving was, on average, 29, 43, 21 and 7% among men, and 28, 38, 26 and 7% among women. Corresponding proportions during leisure were 34, 27, 27 and 13% among men and 28, 27, 32 and 13% among women. Men spent more time sitting relative to non-sitting ([Formula: see text] =0.04, p = 0.03) than women, and less time standing relative to moving ([Formula: see text] =0.07, p = 0.01). At work compared to during leisure, both genders spent more time sitting relative to non-sitting ([Formula: see text] =0.47, p < 0.01); within sitting more time was spent in long relative to short sitting bouts ([Formula: see text] =0.26, p < 0.01), and within non-sitting, more time was spent standing than moving ([Formula: see text] =0.12, p < 0.01). Older workers spent less of their non-sitting time moving than younger workers ([Formula: see text] =0.07, p = 0.01).
CONCLUSION
Male office workers spent more time sitting relative to non-sitting than female workers, and more time moving relative to standing. Both genders were sitting more at work than during leisure. Older workers moved less than younger. These workers could likely benefit from interventions to reduce or break up prolonged sitting time, preferably by moving more.
Topics: Accelerometry; Adult; Age Factors; Data Analysis; Exercise; Female; Humans; Leisure Activities; Male; Middle Aged; Sex Factors; Sitting Position; Standing Position; Sweden; Women, Working; Workplace
PubMed: 32487107
DOI: 10.1186/s12889-020-08909-w -
Medicine and Science in Sports and... Sep 2023Five times sit-to-stand (STS) test is commonly used as a clinical assessment of lower-extremity functional ability, but its association with free-living performance has...
PURPOSE
Five times sit-to-stand (STS) test is commonly used as a clinical assessment of lower-extremity functional ability, but its association with free-living performance has not been studied. Therefore, we investigated the association between laboratory-based STS capacity and free-living STS performance using accelerometry. The results were stratified according to age and functional ability groups.
METHODS
This cross-sectional study included 497 participants (63% women) 60-90 yr old from three independent studies. A thigh-worn triaxial accelerometer was used to estimate angular velocity in maximal laboratory-based STS capacity and in free-living STS transitions over 3-7 d of continuous monitoring. Functional ability was assessed with short physical performance battery.
RESULTS
Laboratory-based STS capacity was moderately associated with the free-living mean and maximal STS performance ( r = 0.52-0.65, P < 0.01). Angular velocity was lower in older compared with younger and in low- versus high-functioning groups, in both capacity and free-living STS variables (all P < 0.05). Overall, angular velocity was higher in capacity compared with free-living STS performance. The STS reserve (test capacity - free-living maximal performance) was larger in younger and in high-functioning groups compared with older and low-functioning groups (all P < 0.05).
CONCLUSIONS
Laboratory-based STS capacity and free-living performance were found to be associated. However, capacity and performance are not interchangeable but rather provide complementary information. Older and low-functioning individuals seemed to perform free-living STS movements at a higher percentage of their maximal capacity compared with younger and high-functioning individuals. Therefore, we postulate that low capacity may limit free-living performance.
Topics: Humans; Adult; Female; Aged; Male; Thigh; Cross-Sectional Studies; Movement; Activities of Daily Living; Accelerometry
PubMed: 37005494
DOI: 10.1249/MSS.0000000000003178 -
Sensors (Basel, Switzerland) Jul 2021Combining accelerometry from multiple independent activity monitors worn by the same subject have gained widespread interest with the assessment of physical activity...
Combining accelerometry from multiple independent activity monitors worn by the same subject have gained widespread interest with the assessment of physical activity behavior. However, a difference in the real time clock accuracy of the activity monitor introduces a substantial temporal misalignment with long duration recordings which is commonly not considered. In this study, a novel method not requiring human interaction is described for the temporal alignment of triaxial acceleration measured with two independent activity monitors and evaluating the performance with the misalignment manually identified. The method was evaluated with free-living recordings using both combined wrist/hip ( = 9) and thigh/hip device ( = 30) wear locations, and descriptive data on initial offset and accumulated day 7 drift in a large-scale population-based study ( = 2513) were calculated. The results from the Bland-Altman analysis show good agreement between the proposed algorithm and the reference suggesting that the described method is valid for reducing the temporal misalignment and thus reduce the measurement error with aggregated data. Applying the algorithm to the = 2513 samples worn for 7-days suggest a wide and substantial issue with drift over time when each subject wears two independent activity monitors.
Topics: Acceleration; Accelerometry; Fitness Trackers; Humans; Motor Activity; Wrist
PubMed: 34300515
DOI: 10.3390/s21144777 -
ELife Mar 2022Body-motion sensors can be used to study non-invasively how animals sleep in the wild, opening up exciting opportunities for comparative analyses across species.
Body-motion sensors can be used to study non-invasively how animals sleep in the wild, opening up exciting opportunities for comparative analyses across species.
Topics: Accelerometry; Animals; Homeostasis; Interpersonal Relations; Sleep
PubMed: 35258454
DOI: 10.7554/eLife.77349 -
Medicine and Science in Sports and... Nov 2021This study aimed to present age- and sex-specific percentiles for daily wrist-worn movement metrics in US youth and adults. This metric represents a summary of all...
PURPOSE
This study aimed to present age- and sex-specific percentiles for daily wrist-worn movement metrics in US youth and adults. This metric represents a summary of all recorded movement, regardless of the purpose, context, or intensity.
METHODS
Wrist-worn accelerometer data from the combined 2011-2014 National Health and Nutrition Examination Survey cycles and the 2012 National Health and Nutrition Examination Survey National Youth Fitness Survey were used for this analysis. Monitor-Independent Movement Summary units (MIMS-units) from raw triaxial accelerometer data were used. We removed the partial first and last assessment days and days with ≥5% nonwear time. Participants with ≥1 valid day were included. Mean MIMS-units were calculated across all valid days. Percentile tables and smoothed curves of daily MIMS-units were calculated for each age and sex using the Generalized Additive Models for Location Shape and Scale.
RESULTS
The analytical sample included 14,705 participants age ≥3 yr. The MIMS-unit activity among youth was similar for both sexes, whereas adult females generally had higher MIMS-unit activity than did males. Median daily MIMS-units peaked at age 6 yr for both sexes (males, 20,613; females, 20,706). Lowest activity was observed for males and females 80+ yr of age: 8799 and 9503, respectively.
CONCLUSIONS
Population referenced MIMS-unit percentiles for US youth and adults are a novel means of characterizing total activity volume. By using MIMS-units, we provide a standardized reference that can be applied across various wrist-worn accelerometer devices. Further work is needed to link these metrics to activity intensity categories and health outcomes.
Topics: Accelerometry; Adolescent; Adult; Age Factors; Aged; Aged, 80 and over; Child; Child, Preschool; Exercise; Female; Fitness Trackers; Health Surveys; Humans; Male; Middle Aged; Reference Values; Reproducibility of Results; United States; Wrist; Young Adult
PubMed: 34115727
DOI: 10.1249/MSS.0000000000002726 -
Experimental Gerontology Aug 2023Estimating lower-limb muscle power during sit-to-stand (STS) tests is feasible for large-scale implementation. This study investigated 1) whether age, functional...
Estimating lower-limb muscle power during sit-to-stand (STS) tests is feasible for large-scale implementation. This study investigated 1) whether age, functional limitations and sex have an influence on the movement strategy and power production during STS; and 2) potential differences between STS power estimated with either a simple equation or a sensor. Five-repetition STS data of 649 subjects (♂352 ♀297) aged 19 to 93 years were included. Subjects were divided in different age groups and levels of functioning. A body-fixed sensor measured (sub)durations, trunk movement (flexion/extension) and STS muscle power (P). Additionally, mean STS muscle power was calculated by a mathematic equation (Alcazar et al., 2018b)Results revealed that 1) older subjects and women showed greater trunk flexion before standing up than younger subjects and men, respectively (both p < 0.001); 2) well-functioning adults seemed to have the tendency to not extend the trunk fully during the sit-to-stand transition (mean difference extension - flexion range = -15.3° to -13.1°, p < 0.001); 3) mobility-limited older adults spent more time in the static sitting and standing positions than their well-functioning counterparts (all p < 0.001); 4) STS power decreased with age and was lower in women and in limited-functioning subjects compared to men and well-functioning subjects, respectively (p < 0.05); 5) P was highly related to P (ICC = 0.902, p < 0.001); and 6) P demonstrated higher values than P in well-functioning adults [mean difference = -0.31 W/kg and -0.22 W/kg for men and women, respectively (p < 0.001)], but not among limited-functioning older adults. To conclude, this study showed that age and functional limitations have an influence on the movement strategy during a 5-repetition STS test. Differences in movement strategy can affect the comparison between P and P. In well-functioning older adults, P was slightly higher than P, which might be related to an incomplete extension in the sit-to-stand transition.
Topics: Male; Humans; Female; Aged; Movement; Lower Extremity; Range of Motion, Articular; Biomechanical Phenomena; Accelerometry
PubMed: 37453590
DOI: 10.1016/j.exger.2023.112255 -
PloS One 2019Surveillance of physical activity at the population level increases the knowledge on levels and trends of physical activity, which may support public health initiatives...
INTRODUCTION
Surveillance of physical activity at the population level increases the knowledge on levels and trends of physical activity, which may support public health initiatives to promote physical activity. Physical activity assessed by accelerometry is challenged by varying data processing procedures, which influences the outcome. We aimed to describe the levels and prevalence estimates of physical activity, and to examine how triaxial and uniaxial accelerometry data influences these estimates, in a large population-based cohort of Norwegian adults.
METHODS
This cross-sectional study included 5918 women and men aged 40-84 years who participated in the seventh wave of the Tromsø Study (2015-16). The participants wore an ActiGraph wGT3X-BT accelerometer attached to the hip for 24 hours per day over seven consecutive days. Accelerometry variables were expressed as volume (counts·minute-1 and steps·day-1) and as minutes per day in sedentary, light physical activity and moderate and vigorous physical activity (MVPA).
RESULTS
From triaxial accelerometry data, 22% (95% confidence interval (CI): 21-23%) of the participants fulfilled the current global recommendations for physical activity (≥150 minutes of MVPA per week in ≥10-minute bouts), while 70% (95% CI: 69-71%) accumulated ≥150 minutes of non-bouted MVPA per week. When analysing uniaxial data, 18% fulfilled the current recommendations (i.e. 20% difference compared with triaxial data), and 55% (95% CI: 53-56%) accumulated ≥150 minutes of non-bouted MVPA per week. We observed approximately 100 less minutes of sedentary time and 90 minutes more of light physical activity from triaxial data compared with uniaxial data (p<0.001).
CONCLUSION
The prevalence estimates of sufficiently active adults and elderly are more than three times higher (22% vs. 70%) when comparing triaxial bouted and non-bouted MVPA. Physical activity estimates are highly dependent on accelerometry data processing criteria and on different definitions of physical activity recommendations, which may influence prevalence estimates and tracking of physical activity patterns over time.
Topics: Accelerometry; Age Distribution; Aged; Cross-Sectional Studies; Exercise; Female; Humans; Male; Middle Aged; Norway; Sedentary Behavior; Wearable Electronic Devices
PubMed: 31794552
DOI: 10.1371/journal.pone.0225670