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Sensors (Basel, Switzerland) Jan 2024Sedentary behaviour (SB) and physical activity (PA) have been shown to be independent modulators of healthy ageing. We thus investigated the impact of activity monitor...
Sedentary behaviour (SB) and physical activity (PA) have been shown to be independent modulators of healthy ageing. We thus investigated the impact of activity monitor placement on the accuracy of detecting SB and PA in older adults, as well as a novel random forest algorithm trained on data from older persons. Four monitor types (ActiGraph wGT3X-BT, ActivPAL3c VT, GENEActiv Original, and DynaPort MM+) were simultaneously worn on five anatomical sites during ten different activities by a sample of twenty older adults (70.0 (12.0) years; 10 women). The results indicated that collecting metabolic equivalent (MET) data for 60 s provided the most representative results, minimising variability. In addition, thigh-worn monitors, including ActivPAL, Random Forest, and Sedentary Sphere-Thigh, exhibited superior performance in classifying SB, with balanced accuracies ≥ 94.2%. Other monitors, such as ActiGraph, DynaPort MM+, and GENEActiv Sedentary Sphere-Wrist, demonstrated lower performance. ActivPAL and GENEActiv Random Forest outperformed other monitors in participant-specific balanced accuracies for SB classification. Only thigh-worn monitors achieved acceptable overall balanced accuracies (≥80.0%) for SB, standing, and medium-to-vigorous PA classifications. In conclusion, it is advisable to position accelerometers on the thigh, collect MET data for ≥60 s, and ideally utilise population-specific trained algorithms.
Topics: Humans; Female; Aged; Aged, 80 and over; Accelerometry; Exercise; Thigh; Wrist; Algorithms
PubMed: 38339613
DOI: 10.3390/s24030895 -
International Journal of Environmental... May 2019. Comparability of accelerometers in epidemiological studies is important for public health researchers. This study aimed to compare physical activity (light, LPA;... (Comparative Study)
Comparative Study
. Comparability of accelerometers in epidemiological studies is important for public health researchers. This study aimed to compare physical activity (light, LPA; moderate, MPA; and moderate-to-vigorous, MVPA) and sedentary behavior (SB) data collected using two Omron triaxial accelerometer generations (Active style Pro, ASP) among a sample of Japanese workers in a free-living environment. . Thirty active and sedentary workers (24-62 years) wore two types of ASP accelerometers, the HJA-350IT (350IT) and the HJA-750C (750C), simultaneously for seven consecutive days to represent a typical week. The accelerometers estimated daily average step counts and time spent per day in LPA, MPA, and MVPA. If a participant had data for ≥4 days (>10 h/day) it was considered valid. The difference and agreement between the two ASPs were analyzed using a paired -test, intra-class correlation coefficients (ICC), and a Bland-Altman analysis in total and for each type of worker. Among all workers, the 750C measured significantly ( < 0.05) less SB, MPA, MVPA, and more LPA compared with the 350IT. The agreements in ICC were high (ICC ≥ 0.94). Compared with the 350IT, the newer generation 750C ASP accelerometer may not provide equivalent estimates of activity time, regardless of the type of physical activity.
Topics: Accelerometry; Adult; Exercise; Female; Humans; Male; Middle Aged; Sedentary Behavior; Time Factors; Young Adult
PubMed: 31067688
DOI: 10.3390/ijerph16091597 -
Scientific Reports Feb 2023Wearable physical activity sensors are widely used in research and practice as they provide objective measures of human behavior at a low cost. An important challenge...
Wearable physical activity sensors are widely used in research and practice as they provide objective measures of human behavior at a low cost. An important challenge for accurate assessment of physical activity behavior in free-living is the detection non-wear. Traditionally, heuristic algorithms that rely on specific interval lengths have been employed to detect non-wear time; however, machine learned models are emerging. We explore the potential of detecting non-wear using decision trees that combine raw acceleration and skin temperature, and we investigate the generalizability of our models, traditional heuristic algorithms, and recently developed machine learned models by external validation. The Decision tree models were trained using one week of data from thigh- and hip-worn accelerometers from 64 children. External validation was performed using data from wrist-worn accelerometers of 42 adolescents. For non-wear episodes longer than 60 min, the heuristic algorithms performed the best with F1-scores above 0.96. However, regarding episodes shorter than 60 min, the best performing method was the decision tree model including the six most important predictors with F1 scores above 0.74 for all sensor locations. We conclude that for classifying non-wear time, researchers should carefully select an appropriate method and we encourage the use of external validation when reporting on machine learned non-wear models.
Topics: Child; Adolescent; Humans; Accelerometry; Exercise; Motor Activity; Wrist; Wrist Joint
PubMed: 36782015
DOI: 10.1038/s41598-023-29666-x -
Sensors (Basel, Switzerland) Feb 2020An improved method of physical activity accelerometer data processing, involving a wider frequency filter than the most commonly used ActiGraph filter, has been shown to...
An improved method of physical activity accelerometer data processing, involving a wider frequency filter than the most commonly used ActiGraph filter, has been shown to better capture variations in physical activity intensity in a lab setting. The aim of the study was to investigate how this improved measure of physical activity affected the relationship with markers of cardiometabolic health. Accelerometer data and markers of cardiometabolic health from 725 adults from two samples, LIV 2013 and SCAPIS pilot, were analyzed. The accelerometer data was processed using both the original ActiGraph method with a low-pass cut-off at 1.6 Hz and the improved method with a low-pass cut-off at 10 Hz. The relationship between the physical activity intensity spectrum and a cardiometabolic health composite score was investigated using partial least squares regression. The strongest association between physical activity and cardiometabolic health was shifted towards higher intensities with the 10 Hz output compared to the ActiGraph method. In addition, the total explained variance was higher with the improved method. The 10 Hz output enables correctly measuring and interpreting high intensity physical activity and shows that physical activity at this intensity is stronger related to cardiometabolic health compared to the most commonly used ActiGraph method.
Topics: Accelerometry; Cardiovascular Diseases; Exercise; Female; Humans; Male; Middle Aged
PubMed: 32085652
DOI: 10.3390/s20041118 -
PloS One 2017Wearable physical activity monitors are growing in popularity and provide the opportunity for large numbers of the public to self-monitor physical activity behaviours....
Wearable physical activity monitors are growing in popularity and provide the opportunity for large numbers of the public to self-monitor physical activity behaviours. The latest generation of these devices feature multiple sensors, ostensibly similar or even superior to advanced research instruments. However, little is known about the accuracy of their energy expenditure estimates. Here, we assessed their performance against criterion measurements in both controlled laboratory conditions (simulated activities of daily living and structured exercise) and over a 24 hour period in free-living conditions. Thirty men (n = 15) and women (n = 15) wore three multi-sensor consumer monitors (Microsoft Band, Apple Watch and Fitbit Charge HR), an accelerometry-only device as a comparison (Jawbone UP24) and validated research-grade multi-sensor devices (BodyMedia Core and individually calibrated Actiheart™). During discrete laboratory activities when compared against indirect calorimetry, the Apple Watch performed similarly to criterion measures. The Fitbit Charge HR was less consistent at measurement of discrete activities, but produced similar free-living estimates to the Apple Watch. Both these devices underestimated free-living energy expenditure (-394 kcal/d and -405 kcal/d, respectively; P<0.01). The multi-sensor Microsoft Band and accelerometry-only Jawbone UP24 devices underestimated most laboratory activities and substantially underestimated free-living expenditure (-1128 kcal/d and -998 kcal/d, respectively; P<0.01). None of the consumer devices were deemed equivalent to the reference method for daily energy expenditure. For all devices, there was a tendency for negative bias with greater daily energy expenditure. No consumer monitors performed as well as the research-grade devices although in some (but not all) cases, estimates were close to criterion measurements. Thus, whilst industry-led innovation has improved the accuracy of consumer monitors, these devices are not yet equivalent to the best research-grade devices or indeed equivalent to each other. We propose independent quality standards and/or accuracy ratings for consumer devices are required.
Topics: Accelerometry; Adult; Calorimetry, Indirect; Energy Metabolism; Exercise; Female; Fitness Trackers; Humans; Male; Reference Values
PubMed: 28234979
DOI: 10.1371/journal.pone.0171720 -
Nutricion Hospitalaria Sep 2014Over the last years, the use of accelerometers has become relevant to quantify physical activity among youth. Methods used with accelerometers might modify the results... (Meta-Analysis)
Meta-Analysis Review
INTRODUCTION
Over the last years, the use of accelerometers has become relevant to quantify physical activity among youth. Methods used with accelerometers might modify the results and the possibility to compare different papers. These devices have been proved to be effective and valid quantifying long periods of physical activity compared to other methods.
OBJECTIVE
To show methodological criteria regarding physical activity assessed by accelerometry with schoolars.
METHODOLOGY
It was conducted a review of the literature related to accelerometers and scholar-aged subjects at PubMed from January 2002 to August 2013, selecting 133 papers.
RESULTS
As far as it is shown, it appears to be some tendencies related to the choice of attachment of the device, wearing time and a shorter epoch-length; however, it has been found a wide variability regarding the model of accelerometer and cutoff points used.
DISCUSSION AND CONCLUSIONS
The different criterion used makes it difficult to compare methodological aspects among studies in spite of some papers carried out similar methods.
Topics: Accelerometry; Adolescent; Child; Exercise; Humans; Life Style; Physical Education and Training; Schools
PubMed: 25561104
DOI: 10.3305/nh.2015.31.1.7450 -
The International Journal of Behavioral... Apr 2024Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers in SB research. The...
BACKGROUND
Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers in SB research. The former measures body movement and the latter measures body posture. The goal of the current study is to quantify the pattern and variation of movement (by ActiGraph activity counts) during activPAL-identified sitting events, and examine associations between patterns and health-related outcomes, such as systolic and diastolic blood pressure (SBP and DBP).
METHODS
The current study included 314 overweight postmenopausal women, who were instructed to wear an activPAL (at thigh) and ActiGraph (at waist) simultaneously for 24 hours a day for a week under free-living conditions. ActiGraph and activPAL data were processed to obtain minute-level time-series outputs. Multilevel functional principal component analysis (MFPCA) was applied to minute-level ActiGraph activity counts within activPAL-identified sitting bouts to investigate variation in movement while sitting across subjects and days. The multilevel approach accounted for the nesting of days within subjects.
RESULTS
At least 90% of the overall variation of activity counts was explained by two subject-level principal components (PC) and six day-level PCs, hence dramatically reducing the dimensions from the original minute-level scale. The first subject-level PC captured patterns of fluctuation in movement during sitting, whereas the second subject-level PC delineated variation in movement during different lengths of sitting bouts: shorter (< 30 minutes), medium (30 -39 minutes) or longer (> 39 minute). The first subject-level PC scores showed positive association with DBP (standardized : 2.041, standard error: 0.607, adjusted p = 0.007), which implied that lower activity counts (during sitting) were associated with higher DBP.
CONCLUSION
In this work we implemented MFPCA to identify variation in movement patterns during sitting bouts, and showed that these patterns were associated with cardiovascular health. Unlike existing methods, MFPCA does not require pre-specified cut-points to define activity intensity, and thus offers a novel powerful statistical tool to elucidate variation in SB patterns and health.
TRIAL REGISTRATION
ClinicalTrials.gov NCT03473145; Registered 22 March 2018; https://clinicaltrials.gov/ct2/show/NCT03473145 ; International Registered Report Identifier (IRRID): DERR1-10.2196/28684.
Topics: Aged; Female; Humans; Middle Aged; Accelerometry; Actigraphy; Blood Pressure; Exercise; Movement; Overweight; Postmenopause; Principal Component Analysis; Sedentary Behavior; Sitting Position; Wearable Electronic Devices
PubMed: 38671485
DOI: 10.1186/s12966-024-01585-8 -
JMIR MHealth and UHealth Oct 2019Wrist-worn activity trackers are popular, and an increasing number of these devices are equipped with heart rate (HR) measurement capabilities. However, the validity of...
BACKGROUND
Wrist-worn activity trackers are popular, and an increasing number of these devices are equipped with heart rate (HR) measurement capabilities. However, the validity of HR data obtained from such trackers has not been thoroughly assessed outside the laboratory setting.
OBJECTIVE
This study aimed to investigate the validity of HR measures of a high-cost consumer-based tracker (Polar A370) and a low-cost tracker (Tempo HR) in the laboratory and free-living settings.
METHODS
Participants underwent a laboratory-based cycling protocol while wearing the two trackers and the chest-strapped Polar H10, which acted as criterion. Participants also wore the devices throughout the waking hours of the following day during which they were required to conduct at least one 10-min bout of moderate-to-vigorous physical activity (MVPA) to ensure variability in the HR signal. We extracted 10-second values from all devices and time-matched HR data from the trackers with those from the Polar H10. We calculated intraclass correlation coefficients (ICCs), mean absolute errors, and mean absolute percentage errors (MAPEs) between the criterion and the trackers. We constructed decile plots that compared HR data from Tempo HR and Polar A370 with criterion measures across intensity deciles. We investigated how many HR data points within the MVPA zone (≥64% of maximum HR) were detected by the trackers.
RESULTS
Of the 57 people screened, 55 joined the study (mean age 30.5 [SD 9.8] years). Tempo HR showed moderate agreement and large errors (laboratory: ICC 0.51 and MAPE 13.00%; free-living: ICC 0.71 and MAPE 10.20%). Polar A370 showed moderate-to-strong agreement and small errors (laboratory: ICC 0.73 and MAPE 6.40%; free-living: ICC 0.83 and MAPE 7.10%). Decile plots indicated increasing differences between Tempo HR and the criterion as HRs increased. Such trend was less pronounced when considering the Polar A370 HR data. Tempo HR identified 62.13% (1872/3013) and 54.27% (5717/10,535) of all MVPA time points in the laboratory phase and free-living phase, respectively. Polar A370 detected 81.09% (2273/2803) and 83.55% (9323/11,158) of all MVPA time points in the laboratory phase and free-living phase, respectively.
CONCLUSIONS
HR data from the examined wrist-worn trackers were reasonably accurate in both the settings, with the Polar A370 showing stronger agreement with the Polar H10 and smaller errors. Inaccuracies increased with increasing HRs; this was pronounced for Tempo HR.
Topics: Accelerometry; Adult; Exercise; Female; Fitness Trackers; Heart Rate; Heart Rate Determination; Humans; Male; Middle Aged; Reproducibility of Results; Singapore; Validation Studies as Topic
PubMed: 31579026
DOI: 10.2196/14120 -
Journal of Cachexia, Sarcopenia and... Apr 2020There has been limited longitudinal assessment of the relationship between moderate-to-vigorous physical activity (MVPA) and sedentary behaviour (SB) with frailty, and...
BACKGROUND
There has been limited longitudinal assessment of the relationship between moderate-to-vigorous physical activity (MVPA) and sedentary behaviour (SB) with frailty, and no studies have explored the possibility of reverse causality. This study aimed to determine the potential bidirectionality of the relationship between accelerometer-assessed MVPA, SB, and frailty over time in older adults.
METHODS
Participants were from the Toledo Study for Healthy Aging. We analysed 186 older people aged 67 to 90 (76.7 ± 3.9; 52.7% female participants) over a 4-year period. Time spent in SB and MVPA was assessed by accelerometry. Frailty Trait Scale was used to determine frailty levels. A cross-lagged panel model design was used to test the reciprocal relationships between MVPA/SB and frailty.
RESULTS
Frailty Trait Scale score changed from 35.4 to 43.8 points between the two times (P < 0.05). We also found a reduction of 7 min/day in the time spent on MVPA (P < 0.05), and participants tended to spend more time on SB (P = 0.076). Our analyses revealed that lower levels of initial MVPA predicted higher levels of later frailty [std. β = -0.126; confidence interval (CI) = -0.231, -0.021; P < 0.05], whereas initial spent time on SB did not predict later frailty (std. β = -0.049; CI = -0.185, 0.087; P = 0.48). Conversely, an initial increased frailty status predicted higher levels of later SB (std. β = 0.167; CI = 0.026, 0.307; P < 0.05) but not those of MVPA (std. β = 0.071; CI = -0.033, 0.175; P = 0.18).
CONCLUSIONS
Our observations suggest that the relationship between MVPA/SB and frailty is unidirectional: individuals who spent less time on MVPA at baseline are more likely to increase their frailty score, and individuals who are more frail are more likely to spent more time on SB at follow-up. Interventions and policies should aim to increase MVPA levels from earlier stages to promote successful aging.
Topics: Accelerometry; Aged; Aged, 80 and over; Female; Frailty; Healthy Aging; Humans; Male
PubMed: 31912990
DOI: 10.1002/jcsm.12511 -
Journal of Physical Activity & Health Jun 2018The purpose of this study was to employ high-frequency accelerometry to explore parent-child physical activity (PA) relationships across a free-living sample.
BACKGROUND
The purpose of this study was to employ high-frequency accelerometry to explore parent-child physical activity (PA) relationships across a free-living sample.
METHODS
We recorded 7 days of wrist-mounted accelerometry data from 168 dyads of elementary-aged children and their parents. Using a custom MATLAB program (Natick, MA), we summed child and parent accelerations over 1 and 60 seconds, respectively, and applied published cut points to determine the amount of time spent in moderate-vigorous PA (MVPA). Bivariate and partial correlations examined parent-child relationships between percentage of time spent in MVPA.
RESULTS
Weak to moderate positive correlations were observed before school (r = .326, P < .001), after school (r = .176, P = .023), during the evening (r = .213, P = .006), and on weekends (r = .231, P = .003). Partial correlations controlling for parent-child MVPA revealed significant relationships during the school day (r = .185, P = .017), before school (r = .315, P < .001), and on weekends (r = .266, P = .001). In addition, parents of more active children were significantly more active than parents of less active children during the evening.
CONCLUSIONS
These data suggest that there is some association between parent-child PA, especially before school and on weekends. Future interventions aiming to increase PA among adults and children must consider patterns of MVPA specific to children and parents and target them accordingly.
Topics: Accelerometry; Adult; Child; Exercise; Female; Humans; Male; Parent-Child Relations; Parents; Schools; Wrist
PubMed: 29570002
DOI: 10.1123/jpah.2016-0645