-
Sensors (Basel, Switzerland) Apr 2022Background: Previous research has explored associations between accelerometry and Global Navigation Satellite System (GNSS) derived loads. However, to our knowledge, no...
Background: Previous research has explored associations between accelerometry and Global Navigation Satellite System (GNSS) derived loads. However, to our knowledge, no study has investigated the relationship between these measures and a known distance. Thus, the current study aimed to assess and compare the ability of four accelerometry based metrics and GNSS to predict known distance completed using different movement constraints. Method: A correlational design study was used to evaluate the association between the dependent and independent variables. A total of 30 physically active college students participated. Participants were asked to walk two different known distances (DIST) around a 2 m diameter circle (small circle) and a different distance around an 8 m diameter circle (large circle). Each distance completed around the small circle by one participant was completed around the large circle by a different participant. The same 30 distances were completed around each circle and ranged from 12.57 to 376.99 m. Instrumentation: Acceleration data was collected via a tri-axial accelerometer sampling at 100 Hz. Accelerometry derived measures included the sum of the absolute values of acceleration (SUM), the square root of the sum of squared accelerations (MAG), Player Load (PL), and Impulse Load (IL). Distance (GNSSD) was measured from positional data collected using a triple GNSS unit sampling at 10 Hz. Results: Separate simple linear regression models were created to assess the ability of each independent variable to predict DIST. The results indicate that all regression models performed well (R = 0.960−0.999, R2 = 0.922−0.999; RMSE = 0.047−0.242, p < 0.001), while GNSSD (small circle, R = 0.999, R2 = 0.997, RMSE = 0.047 p < 0.001; large circle, R = 0.999, R2 = 0.999, RMSE = 0.027, p < 0.001) and the accelerometry derived metric MAG (small circle, R = 0.992, R2 = 0.983, RMSE = 0.112, p < 0.001; large circle, R = 0.997, R2 = 0.995, RMSE = 0.064, p < 0.001) performed best among all models. Conclusions: This research illustrates that both GNSS and accelerometry may be used to indicate total distance completed while walking.
Topics: Acceleration; Accelerometry; Data Collection; Humans; Linear Models; Walking
PubMed: 35591051
DOI: 10.3390/s22093360 -
British Journal of Sports Medicine Jul 2014The technology and application of current accelerometer-based devices in physical activity (PA) research allow the capture and storage or transmission of large volumes... (Review)
Review
The technology and application of current accelerometer-based devices in physical activity (PA) research allow the capture and storage or transmission of large volumes of raw acceleration signal data. These rich data not only provide opportunities to improve PA characterisation, but also bring logistical and analytic challenges. We discuss how researchers and developers from multiple disciplines are responding to the analytic challenges and how advances in data storage, transmission and big data computing will minimise logistical challenges. These new approaches also bring the need for several paradigm shifts for PA researchers, including a shift from count-based approaches and regression calibrations for PA energy expenditure (PAEE) estimation to activity characterisation and EE estimation based on features extracted from raw acceleration signals. Furthermore, a collaborative approach towards analytic methods is proposed to facilitate PA research, which requires a shift away from multiple independent calibration studies. Finally, we make the case for a distinction between PA represented by accelerometer-based devices and PA assessed by self-report.
Topics: Accelerometry; Consensus; Diffusion of Innovation; Exercise; Humans; Monitoring, Ambulatory; Nutrition Surveys; Self Report
PubMed: 24782483
DOI: 10.1136/bjsports-2014-093546 -
Journal of Physical Activity & Health Apr 2021The measurement of daily physical activity (DPA) is important for the prognosis and quantifying clinical outcomes in individuals with heart disease. The measurement of... (Review)
Review
BACKGROUND
The measurement of daily physical activity (DPA) is important for the prognosis and quantifying clinical outcomes in individuals with heart disease. The measurement of DPA is more feasible using subjective measures when compared with objective measures. The purpose of this systematic review of the literature was to identify the subjective measures of DPA that have established reliability and validity in individuals with heart disease to assist clinician and researcher instrument selection.
METHODS
A systematic search of PubMed, CINAHL, MEDLINE, and ProQuest databases was performed. Methodological rigor was assessed using 3 different quality appraisal tools. Qualitative synthesis of included studies was performed.
RESULTS
Twenty-two unique studies covering 19 subjective DPA measures were ultimately included. Methodological rigor was generally fair, and validity coefficients were moderate at best.
CONCLUSIONS
Only 4 subjective measures that have established test-retest reliability and that provide an estimate of energy expenditure, metabolic equivalents, or minutes of DPA were compared against accelerometry or a DPA diary in patients with heart disease: SWISS Physical Activity Questionnaire, Total Activity Measure 1 and 2, and Mobile Physical Activity Logger. Depending on the clinician or researcher needs, instrument selection would depend on the recall period and the DPA construct being measured.
Topics: Accelerometry; Exercise; Heart Diseases; Humans; Reproducibility of Results
PubMed: 33668019
DOI: 10.1123/jpah.2020-0661 -
Medicine and Science in Sports and... Aug 2019A lack of standardization with accelerometry-based monitors has made it hard to advance applications for both research and practice. Resolving these challenges is... (Review)
Review
INTRODUCTION
A lack of standardization with accelerometry-based monitors has made it hard to advance applications for both research and practice. Resolving these challenges is essential for developing methods for consistent, agnostic reporting of physical activity outcomes from wearable monitors in clinical applications.
METHODS
This article reviewed the literature on the methods used to evaluate the validity of contemporary consumer activity monitors. A rationale for focusing on energy expenditure as a key outcome measure in validation studies was provided followed by a summary of the strengths and limitations of different analytical methods. The primary review included 23 recent validation studies that collectively reported energy expenditure estimates from 58 monitors relative to values from appropriate criterion measures.
RESULTS
The majority of studies reported weak indicators such as correlation coefficients (87%), but only half (52%) reported the recommended summary statistic of mean absolute percent error needed to evaluate actual individual error. Fewer used appropriate tests of agreement such as equivalence testing (22%).
CONCLUSIONS
The use of inappropriate analytic methods and incomplete reporting of outcomes is a major limitation for systematically advancing research with both research grade and consumer-grade activity monitors. Guidelines are provided to standardize analytic methods and reporting in these types of studies to enhance the utility of the devices for clinical mHealth applications.
Topics: Accelerometry; Calibration; Data Interpretation, Statistical; Energy Metabolism; Exercise; Fitness Trackers; Health Behavior; Humans; Research Design; Validation Studies as Topic
PubMed: 30913159
DOI: 10.1249/MSS.0000000000001966 -
American Journal of Preventive Medicine Jun 2017Accurate tracking of physical activity (PA) and sedentary behavior (SB) is important to advance public health, but little is known about how to interpret wrist-worn...
INTRODUCTION
Accurate tracking of physical activity (PA) and sedentary behavior (SB) is important to advance public health, but little is known about how to interpret wrist-worn accelerometer data. This study compares youth estimates of SB and moderate to vigorous PA (MVPA) obtained using raw and count-based processing methods.
METHODS
Data were collected between April and October 2014 for the National Cancer Institute's Family Life, Activity, Sun, Health, and Eating Study: a cross-sectional Internet-based study of youth/family cancer prevention behaviors. A subsample of 628 adolescents (aged 12-17 years) wore the ActiGraph GT3X+ on the wrist for 7 days. In 2015-2016, SB and MVPA time were calculated from raw data using R-package GGIR and from activity counts data using published cutpoints (Crouter and Chandler). Estimates were compared across age, sex, and weight status to examine the impact of processing methods on behavioral outcomes.
RESULTS
ActiGraph data were available for 408 participants. Large differences in SB and MVPA time were observed between processing methods, but age and gender patterns were similar. Younger children (aged 12-14 years) had lower sedentary time and greater MVPA time (p-values <0.05) than older children (aged 15-17 years), consistent across methods. The proportion of youth with ≥60 minutes of MVPA/day was highest with the Crouter methods (~50%) and lowest with GGIR (~0%).
CONCLUSIONS
Conclusions about youth PA and SB are influenced by the wrist-worn accelerometer data processing method. Efforts to harmonize processing methods are needed to promote standardization and facilitate reporting of monitor-based PA data.
Topics: Accelerometry; Adolescent; Age Factors; Child; Cross-Sectional Studies; Exercise; Female; Health Behavior; Humans; Male; Sedentary Behavior; Wrist
PubMed: 28526364
DOI: 10.1016/j.amepre.2017.01.012 -
European Journal of Sport Science Feb 2022This study compared and calibrated metabolic equivalents (METs) per day from 24-hour physical activity recall (24hPAR) with accelerometry. A sub-sample of 74 adults of...
This study compared and calibrated metabolic equivalents (METs) per day from 24-hour physical activity recall (24hPAR) with accelerometry. A sub-sample of 74 adults of both sexes, residents of Brasília, Brazil, from a larger study had same day measurements of accelerometry and 24hPAR data. METs values were assessed by accelerometry (7 consecutive days of use) and by 24hPAR (minimum of one and maximum of 2 per person). A script was written in the R statistical software to analyse the recall and accelerometer data. The script ran a simple linear regression to visualize the relationship between total METs/day for the two methods and to execute the recall measurement error correction. Most of participants were female (54.1%), with at least university graduate (94.6%) and mean age of 34.8 years (±11.83). The correlation coefficient obtained between 24hPAR and accelerometer was = 0.55, considered moderate and significant ( < 0.001). A majority of the participants (77%) underestimated METs values compared to accelerometry when answering the questionnaire. Calibration of 24hPAR allowed us to approximate MET values to the accelerometer. The calibration equation to correct total METs/day for measurement error is (total 24hPAR METs/day - 10.6)/0.619. The 24hPAR is a decent tool to assess PA level in large adults' samples. However, compared with accelerometer, it underestimates METs values, which can be corrected with the use of the calibration equation provided in this study.
Topics: Accelerometry; Adult; Calibration; Exercise; Female; Humans; Male; Mental Recall; Metabolic Equivalent
PubMed: 33327887
DOI: 10.1080/17461391.2020.1866077 -
Medicine and Science in Sports and... Jan 2017The way physical activity (PA) and sedentary behavior (SB) are accumulated throughout the day (i.e., patterns) may be important for health, but identifying measurable... (Randomized Controlled Trial)
Randomized Controlled Trial
INTRODUCTION
The way physical activity (PA) and sedentary behavior (SB) are accumulated throughout the day (i.e., patterns) may be important for health, but identifying measurable and meaningful metrics of behavioral patterns is challenging. This study evaluated accelerometer-derived metrics to determine whether they predicted PA and SB patterns and were reliably measured.
METHODS
We defined and measured 55 metrics that describe daily PA and SB using data collected by using the activPAL monitor in four studies. The first two studies were randomized crossover designs that included recreationally active participants. Study 1 experimentally manipulated time spent in moderate-to-vigorous-intensity PA and sedentary time, and study 2 held time in exercise constant and manipulated SB. Study 3 included inactive participants who increased exercise, decreased sedentary time, or both. The study conditions induced distinct behavioral patterns; thus, we tested whether the new metrics could improve the prediction of an individual's study condition after adjusting for the overall volume of PA or SB using conditional logistic regression. In study 4, we measured the 3-month reliability for the pattern metrics by calculating intraclass correlation coefficients in a community-dwelling sample who wore the activPAL monitor twice for 7 d.
RESULTS
In each of the experimental studies, we identified new metrics that could improve the accuracy for predicting condition beyond SB and moderate-to-vigorous-intensity PA volume. In study 1, 23 metrics were predictive of a highly active condition, and in study 2, 24 metrics were predictive of a highly sedentary condition. In study 4, the median intraclass correlation coefficients (25-75th percentiles) of the metrics were 0.59 (0.46-0.65).
CONCLUSIONS
Several new metrics were predictive of patterns of SB, exercise, and nonexercise behavior and are moderately reliable for a 3-month period. Applying these metrics to determine whether daily behavioral patterns are associated with health-outcomes is an important area of future research.
Topics: Accelerometry; Adult; Cross-Over Studies; Exercise; Health Behavior; Humans; Sedentary Behavior; Surveys and Questionnaires
PubMed: 27992396
DOI: 10.1249/MSS.0000000000001073 -
Annals of Epidemiology Mar 2019We evaluated the validity and sensitivity to change of a workplace questionnaire to assess sedentary behavior (SB) during and outside work.
PURPOSE
We evaluated the validity and sensitivity to change of a workplace questionnaire to assess sedentary behavior (SB) during and outside work.
METHODS
Participants wore an activPAL and completed an SB questionnaire at two time points (baseline and 3-month follow-up). Ecological momentary assessments were used to assess workplace location (at desk vs. away from desk). Intraclass correlation coefficients, mean difference, root of mean square error, kappa agreement, and Bland-Altman plots assessed validity. Sensitivity to change after 3 months of intervention was assessed using the standardized effect size.
RESULTS
Data from 546 participants (age = 45.1 ± 16.4 years, 24.9% males, 72.7% white) were analyzed. Intraclass correlation coefficients ranged from 0.08 to 0.23. SB was overestimated d¯(95%CI)[] by 47.9 (39.2, 56.6) min during work hours but underestimated for both non-work hours and nonworkdays by -38.3 (-47.4, 29.1) and -106.7 (124.0, -89.5) min, respectively. Participants slightly underestimated SB by -3.4 (-12.6, 5.7)% when at their desk but overestimated SB by 2.8 (-2.4, 8.0)% when not at their desk. The questionnaire demonstrated similar standardized effect size (>0.6) to the activPAL for sedentary and standing time.
CONCLUSIONS
Agreement between the questionnaire and activPAL was on par with other self-report measures. The questionnaire yielded valid estimates of at/away from desk SB and was sensitive to change.
Topics: Accelerometry; Adult; Arizona; Exercise; Female; Humans; Male; Middle Aged; Minnesota; Reproducibility of Results; Sedentary Behavior; Self Report; Surveys and Questionnaires; Urban Population; Workplace
PubMed: 30718055
DOI: 10.1016/j.annepidem.2019.01.002 -
JAMA Network Open Apr 2019Affordable, quantitative methods to screen children for developmental delays are needed. Motor milestones can be an indicator of developmental delay and may be used to...
IMPORTANCE
Affordable, quantitative methods to screen children for developmental delays are needed. Motor milestones can be an indicator of developmental delay and may be used to track developmental progress. Accelerometry offers a way to gather real-world information about pediatric motor behavior.
OBJECTIVE
To develop a referent cohort of pediatric accelerometry from bilateral upper extremities (UEs) and determine whether movement can accurately distinguish those with and without motor deficits.
DESIGN, SETTING, AND PARTICIPANTS
Children aged 0 to 17 years participated in a prospective cohort from December 8, 2014, to December 29, 2017. Children were recruited from Ranken Jordan Pediatric Bridge Hospital, Maryland Heights, Missouri, and Washington University School of Medicine in St Louis, St Louis, Missouri. Typically developing children were included as a referent cohort if they had no history of motor or neurological deficit; consecutive sampling and matching ensured equal representation of sex and age. Children with diagnosed asymmetric motor deficits were included in the motor impaired cohort.
EXPOSURES
Bilateral UE motor activity was measured using wrist-worn accelerometers for a total of 100 hours in 25-hour increments.
MAIN OUTCOMES AND MEASURES
To characterize bilateral UE motor activity in a referent cohort for the purpose of detecting irregularities in the future, total activity and the use ratio between UEs were used to describe typically developing children. Asymmetric impairment was classified using the mono-arm use index (MAUI) and bilateral-arm use index (BAUI) to quantify the acceleration of unilateral movements.
RESULTS
A total of 216 children enrolled, and 185 children were included in analysis. Of these, 156 were typically developing, with mean (SD) age 9.1 (5.1) years and 81 boys (52.0%). There were 29 children in the motor impaired cohort, with mean (SD) age 7.4 (4.4) years and 16 boys (55.2%). The combined MAUI and BAUI (mean [SD], 0.86 [0.005] and use ratio (mean [SD], 0.90 [0.008]) had similar F1 values. The area under the curve was also similar between the combined MAUI and BAUI (mean [SD], 0.98 [0.004]) and the use ratio (mean [SD], 0.98 [0.004]).
CONCLUSIONS AND RELEVANCE
Bilateral UE movement as measured with accelerometry may provide a meaningful metric of real-world motor behavior across childhood. Screening in early childhood remains a challenge; MAUI may provide an effective method for clinicians to measure and visualize real-world motor behavior in children at risk for asymmetrical deficits.
Topics: Accelerometry; Adolescent; Child; Child, Preschool; Developmental Disabilities; Female; Humans; Infant; Infant, Newborn; Male; Motor Activity; Movement; Prospective Studies; Reference Values; Reproducibility of Results; Upper Extremity
PubMed: 31026032
DOI: 10.1001/jamanetworkopen.2019.2970 -
PloS One 2016Accelerometers have been widely deployed in public health studies in recent years. While they collect high-resolution acceleration signals (e.g., 10-100 Hz), research...
Accelerometers have been widely deployed in public health studies in recent years. While they collect high-resolution acceleration signals (e.g., 10-100 Hz), research has mainly focused on summarized metrics provided by accelerometers manufactures, such as the activity count (AC) by ActiGraph or Actical. Such measures do not have a publicly available formula, lack a straightforward interpretation, and can vary by software implementation or hardware type. To address these problems, we propose the physical activity index (AI), a new metric for summarizing raw tri-axial accelerometry data. We compared this metric with the AC and another recently proposed metric for raw data, Euclidean Norm Minus One (ENMO), against energy expenditure. The comparison was conducted using data from the Objective Physical Activity and Cardiovascular Health Study, in which 194 women 60-91 years performed 9 lifestyle activities in the laboratory, wearing a tri-axial accelerometer (ActiGraph GT3X+) on the hip set to 30 Hz and an Oxycon portable calorimeter, to record both tri-axial acceleration time series (converted into AI, AC, and ENMO) and oxygen uptake during each activity (converted into metabolic equivalents (METs)) at the same time. Receiver operating characteristic analyses indicated that both AI and ENMO were more sensitive to moderate and vigorous physical activities than AC, while AI was more sensitive to sedentary and light activities than ENMO. AI had the highest coefficients of determination for METs (0.72) and was a better classifier of physical activity intensity than both AC (for all intensity levels) and ENMO (for sedentary and light intensity). The proposed AI provides a novel and transparent way to summarize densely sampled raw accelerometry data, and may serve as an alternative to AC. The AI's largely improved sensitivity on sedentary and light activities over AC and ENMO further demonstrate its advantage in studies with older adults.
Topics: Accelerometry; Aged; Aged, 80 and over; Energy Metabolism; Exercise; Female; Humans; Middle Aged; ROC Curve; Software
PubMed: 27513333
DOI: 10.1371/journal.pone.0160644