scholarly journals The Dilemma of Analyzing Physical Activity and Sedentary Behavior with Wrist Accelerometer Data: Challenges and Opportunities

2021 ◽  
Vol 10 (24) ◽  
pp. 5951
Author(s):  
Zan Gao ◽  
Wenxi Liu ◽  
Daniel J. McDonough ◽  
Nan Zeng ◽  
Jung Eun Lee
Keyword(s):  
Physical Activity ◽  
Sedentary Behavior ◽  
Behavioral Science ◽  
Accelerometer Data ◽  
Free Living ◽  
Activity Data ◽  
Future Studies ◽  
Acceleration Data ◽  
Challenges And Opportunities ◽  
The Times

Physical behaviors (e.g., physical activity and sedentary behavior) have been the focus among many researchers in the biomedical and behavioral science fields. The recent shift from hip- to wrist-worn accelerometers in these fields has signaled the need to develop novel approaches to process raw acceleration data of physical activity and sedentary behavior. However, there is currently no consensus regarding the best practices for analyzing wrist-worn accelerometer data to accurately predict individuals’ energy expenditure and the times spent in different intensities of free-living physical activity and sedentary behavior. To this end, accurately analyzing and interpreting wrist-worn accelerometer data has become a major challenge facing many clinicians and researchers. In response, this paper attempts to review different methodologies for analyzing wrist-worn accelerometer data and offer cutting edge, yet appropriate analysis plans for wrist-worn accelerometer data in the assessment of physical behavior. In this paper, we first discuss the fundamentals of wrist-worn accelerometer data, followed by various methods of processing these data (e.g., cut points, steps per minute, machine learning), and then we discuss the opportunities, challenges, and directions for future studies in this area of inquiry. This is the most comprehensive review paper to date regarding the analysis and interpretation of free-living physical activity data derived from wrist-worn accelerometers, aiming to help establish a blueprint for processing wrist-derived accelerometer data.

scholarly journals The Validity of MotionSense HRV in Estimating Sedentary Behavior and Physical Activity under Free-Living and Simulated Activity Settings

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1411
Author(s):  
Sunku Kwon ◽  
Neng Wan ◽  
Ryan D. Burns ◽  
Timothy A. Brusseau ◽  
Youngwon Kim ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sedentary Behavior ◽  
Living Conditions ◽  
Systematic Bias ◽  
Free Living ◽  
Activity Settings ◽  
Equivalence Tests ◽  
Acceleration Data ◽  
Promising Application ◽  
Free Living Conditions

MotionSense HRV is a wrist-worn accelerometery-based sensor that is paired with a smartphone and is thus capable of measuring the intensity, duration, and frequency of physical activity (PA). However, little information is available on the validity of the MotionSense HRV. Therefore, the purpose of this study was to assess the concurrent validity of the MotionSense HRV in estimating sedentary behavior (SED) and PA. A total of 20 healthy adults (age: 32.5 ± 15.1 years) wore the MotionSense HRV and ActiGraph GT9X accelerometer (GT9X) on their non-dominant wrist for seven consecutive days during free-living conditions. Raw acceleration data from the devices were summarized into average time (min/day) spent in SED and moderate-to-vigorous PA (MVPA). Additionally, using the Cosemed K5 indirect calorimetry system (K5) as a criterion measure, the validity of the MotionSense HRV was examined in simulated free-living conditions. Pearson correlations, mean absolute percent errors (MAPE), Bland–Altman (BA) plots, and equivalence tests were used to examine the validity of the MotionSense HRV against criterion measures. The correlations between the MotionSense HRV and GT9X were high and the MAPE were low for both the SED (r = 0.99, MAPE = 2.4%) and MVPA (r = 0.97, MAPE = 9.1%) estimates under free-living conditions. BA plots illustrated that there was no systematic bias between the MotionSense HRV and criterion measures. The estimates of SED and MVPA from the MotionSense HRV were significantly equivalent to those from the GT9X; the equivalence zones were set at 16.5% for SED and 29% for MVPA. The estimates of SED and PA from the MotionSense HRV were less comparable when compared with those from the K5. The MotionSense HRV yielded comparable estimates for SED and PA when compared with the GT9X accelerometer under free-living conditions. We confirmed the promising application of the MotionSense HRV for monitoring PA patterns for practical and research purposes.

CARL: a running recognition algorithm for free-living accelerometer data

2021 ◽  
Author(s):  
John J Davis IV ◽  
Marcin Straczkiewicz ◽  
Jaroslaw Harezlak ◽  
Allison H Gruber
Keyword(s):  
Physical Activity ◽  
Confidence Interval ◽  
Open Source ◽  
Recognition Algorithm ◽  
Physical Activities ◽  
Great Promise ◽  
Accelerometer Data ◽  
Free Living ◽  
Acceleration Data ◽  
Wearable Accelerometers

Abstract Wearable accelerometers hold great promise for physical activity epidemiology and sports biomechanists. However, identifying and extracting data from specific physical activities, such as running, remains challenging. Objective: To develop and validate an algorithm to identify bouts of running in raw, free-living accelerometer data from devices worn at the wrist or torso (waist, hip, chest). Approach: The CARL (continuous amplitude running logistic) classifier identifies acceleration data with amplitude and frequency characteristics consistent with running. The CARL classifier was trained on data from 31 adults wearing accelerometers on the waist and wrist, then validated on free-living data from 30 new, unseen subjects plus 166 subjects from previously-published datasets using different devices, wear locations, and sample frequencies. Main Results: On free-living data, the CARL classifier achieved mean accuracy (F1 score) of 0.984 (95% confidence interval 0.962-0.996) for data from the waist and 0.994 (95% CI 0.991-0.996) for data from the wrist. In previously-published datasets, the CARL classifier identified running with mean accuracy (F1 score) of 0.861 (95% CI 0.836-0.884) for data from the chest, 0.911 (95% CI 0.884-0.937) for data from the hip, 0.916 (95% CI 0.877-0.948) for data from the waist, and 0.870 (95% CI 0.834-0.903) for data from the wrist. Misclassification primarily occurred during activities with similar torso acceleration profiles to running, such as rope jumping and elliptical machine use. Significance: The CARL classifier can accurately identify bouts of running as short as three seconds in free-living accelerometry data. An open-source implementation of the CARL classifier is available at <<GITHUBURL>>.

scholarly journals Moving Forward With Accelerometer-Assessed Physical Activity: Two Strategies to Ensure Meaningful, Interpretable, and Comparable Measures

2018 ◽  
Vol 30 (4) ◽  
pp. 450-456 ◽  
Author(s):  
Alex V. Rowlands
Keyword(s):  
Physical Activity ◽  
Black Box ◽  
Data Sets ◽  
Accelerometer Data ◽  
Future Health ◽  
The Past ◽  
Acceleration Data ◽  
Night And Day ◽  
Very High ◽  
Made In

Significant advances have been made in the measurement of physical activity in youth over the past decade. Monitors and protocols promote very high compliance, both night and day, and raw measures are available rather than “black box” counts. Consequently, many surveys and studies worldwide now assess children’s physical behaviors (physical activity, sedentary behavior, and sleep) objectively 24 hours a day, 7 days a week using accelerometers. The availability of raw acceleration data in many of these studies is both an opportunity and a challenge. The richness of the data lends itself to the continued development of innovative metrics, whereas the removal of proprietary outcomes offers considerable potential for comparability between data sets and harmonizing data. Using comparable physical activity outcomes could lead to improved precision and generalizability of recommendations for children’s present and future health. The author will discuss 2 strategies that he believes may help ensure comparability between studies and maximize the potential for data harmonization, thereby helping to capitalize on the growing body of accelerometer data describing children’s physical behaviors.

scholarly journals Development and Evaluation of an Accelerometer-Based Protocol for Measuring Physical Activity Levels in Cancer Survivors: Development and Usability Study

10.2196/18491 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e18491
Author(s):  
Tracy E Crane ◽  
Meghan B Skiba ◽  
Austin Miller ◽  
David O Garcia ◽  
Cynthia A Thomson
Keyword(s):  
Physical Activity ◽  
Ovarian Cancer ◽  
Data Collection ◽  
Cancer Survivors ◽  
Accelerometer Data ◽  
Activity Tracking ◽  
Activity Data ◽  
Convenience Sample ◽  
Time Points ◽  
Activity Measurements

Background The collection of self-reported physical activity using validated questionnaires has known bias and measurement error. Objective Accelerometry, an objective measure of daily activity, increases the rigor and accuracy of physical activity measurements. Here, we describe the methodology and related protocols for accelerometry data collection and quality assurance using the Actigraph GT9X accelerometer data collection in a convenience sample of ovarian cancer survivors enrolled in GOG/NRG 0225, a 24-month randomized controlled trial of diet and physical activity intervention versus attention control. Methods From July 2015 to December 2019, accelerometers were mailed on 1337 separate occasions to 580 study participants to wear at 4 time points (baseline, 6, 12, and 24 months) for 7 consecutive days. Study staff contacted participants via telephone to confirm their availability to wear the accelerometers and reviewed instructions and procedures regarding the return of the accelerometers and assisted with any technology concerns. Results We evaluated factors associated with wear compliance, including activity tracking, use of a mobile app, and demographic characteristics with chi-square tests and logistic regression. Compliant data, defined as ≥4 consecutive days with ≥10 hours daily wear time, exceeded 90% at all study time points. Activity tracking, but no other characteristics, was significantly associated with compliant data at all time points (P<.001). This implementation of data collection through accelerometry provided highly compliant and usable activity data in women who recently completed treatment for ovarian cancer. Conclusions The high compliance and data quality associated with this protocol suggest that it could be disseminated to support researchers who seek to collect robust objective activity data in cancer survivors residing in a wide geographic area.

scholarly journals Capturing the Pattern of Physical Activity and Sedentary Behavior: Exposure Variation Analysis of Accelerometer Data

2014 ◽  
Vol 11 (3) ◽  
pp. 614-625 ◽  
Author(s):  
Leon Straker ◽  
Amity Campbell ◽  
Svend Erik Mathiassen ◽  
Rebecca Anne Abbott ◽  
Sharon Parry ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sedentary Behavior ◽  
Public Health Research ◽  
Office Workers ◽  
Accelerometer Data ◽  
Variation Analysis ◽  
Occupational Groups ◽  
Time Pattern ◽  
Exposure Variation Analysis ◽  
First Time

Background:Capturing the complex time pattern of physical activity (PA) and sedentary behavior (SB) using accelerometry remains a challenge. Research from occupational health suggests exposure variation analysis (EVA) could provide a meaningful tool. This paper (1) explains the application of EVA to accelerometer data, (2) demonstrates how EVA thresholds and derivatives could be chosen and used to examine adherence to PA and SB guidelines, and (3) explores the validity of EVA outputs.Methods:EVA outputs are compared with accelerometer data from 4 individuals (Study 1a and1b) and 3 occupational groups (Study 2): seated workstation office workers (n = 8), standing workstation office workers (n = 8), and teachers (n = 8).Results:Line graphs and related EVA graphs highlight the use of EVA derivatives for examining compliance with guidelines. EVA derivatives of occupational groups confirm no difference in bouts of activity but clear differences as expected in extended bouts of SB and brief bursts of activity, thus providing evidence of construct validity.Conclusions:EVA offers a unique and comprehensive generic method that is able, for the first time, to capture the time pattern (both frequency and intensity) of PA and SB, which can be tailored for both occupational and public health research.

Abstract MP01: Association of Objectively-measured Physical Activity with Cardiovascular Risk in Mobility-limited Older Adults

Circulation ◽  
2014 ◽  
Vol 129 (suppl_1) ◽  
Author(s):  
Thomas W Buford ◽  
Don G Hire ◽  
Walter T Ambrosius ◽  
Stephen D Anton ◽  
Timothy S Church ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Sedentary Behavior ◽  
Risk Score ◽  
Activity Data ◽  
Mean Intensity ◽  
Wear Time ◽  
Low Intensity ◽  
Objectively Measured ◽  
The Impact

Introduction: In middle-aged adults, time spent being sedentary is associated with cardiovascular (CV) health risks independent of structured physical activity (PA). However, data are sparse regarding the impact of sedentary behavior on CV risk in older adults. The extent to which the absolute duration or intensity of daily PA reduces CV risk in older adults is also unknown. Objectives: Our objective was to examine the cross-sectional association between objectively-measured sedentary behavior and predicted CV risk among older adults in the Lifestyle Interventions and Independence for Elders (LIFE) study. The secondary objective was to evaluate associations between the duration/intensity of daily PA and predicted CV risk. Methods: LIFE is a randomized clinical trial to determine if regular PA prevents mobility disability among mobility-limited older adults. Activity data were collected by hip-worn accelerometer at baseline prior to participation in study interventions. Only participants with at least three days of accelerometry data (≥ 10 hrs wear time) were included. Unadjusted and adjusted linear regression was used to model the relationship between accelerometry measures and predicted 10-year Framingham risk of Hard Coronary Heart Disease (HCHD; i.e. myocardial infarction or coronary death). Adjusted models included demographic confounders (e.g. education, race, income) and health parameters (e.g. depression, cognition, arthritis) not in the risk score. Accelerometry cut-points were (in counts/min): sedentary behavior: < 100; low-intensity activity: 100-499; higher intensity activity: > 500. Results: Participants (n = 1170; 78.7 ± [SD] 5.3 years; 66.1% female) had a median HCHD risk of 10.3% (25 th -75 th %: 5.7-18.6). Over a mean accelerometer wear time of 8.1 ± 3.2 days, participants spent 77.0 ± 8.2% of their time sedentary. They also spent 16.6 ± 5.0% of their time in low-intensity PA and 6.4 ± 4.4% in higher-intensity PA. For all PA performed (> 100 counts/min), participants achieved a median of 393.4 (337.8-473.5) counts/min. In the unadjusted model, time spent sedentary (β = 2.41; 95% CI : 1.94, 2.89), in low-intensity PA (-2.56; -3.03, -2.08), and in higher-intensity PA (-1.60; -2.09, -1.11) were all associated with HCHD risk (all p’s < 0.001). These associations remained significant after adjustment. The mean intensity of daily PA was not significantly associated with HCHD risk in any model (p > 0.05). Conclusions: Daily time spent being sedentary is positively associated with predicted 10-year HCHD risk among mobility-limited older adults. Duration, but not mean intensity, of daily PA is inversely associated with HCHD risk score in this population.

scholarly journals Cross-Country Comparisons of Physical Activity and Sedentary Behavior among 5-Year-Old Children

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Kerry L. McIver ◽  
Russell R. Pate ◽  
Marsha Dowda ◽  
Suzanne Bennett Johnson ◽  
Jimin Yang ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sedentary Behavior ◽  
Genetic Risk ◽  
Demographic Characteristics ◽  
Washington State ◽  
Future Studies ◽  
Country Differences ◽  
Cross Country ◽  
The U.S

Purpose. Previous studies have observed that physical activity (PA) levels tend to be lower in the U.S. population than in many other countries. Within the U.S., PA levels in children are lower in the South than in other regions. Cross-country and interregional differences in PA have not been studied in young children. Methods. In an ongoing study of children at genetic risk for Type 1 diabetes, PA was measured by accelerometry in samples of 5-year-old children (n=2008) from Finland (n=370), Germany (n=85), Sweden (n=706), and the U.S. (n=847). The U.S. sample was drawn from centers in Washington State, Colorado, and Georgia/Florida. Children wore accelerometers for 7 days, and the data were reduced to daily minutes of light-, moderate- (MPA), vigorous- (VPA), and moderate-to-vigorous- (MVPA) intensity PA and sedentary behavior. Multiple regression was used to compare children across countries and across regions in the U.S, adjusting for wear time, body mass index, and demographic characteristics. Results. After adjusting for previously mentioned factors, MVPA and MPA were lower in U.S. children than those in Finland and Sweden. Estimates of physical activity were higher in Finland than in other countries, although not all comparisons were significantly different. U.S children spent significantly more time in sedentary behavior than children in Finland (p<0.0001). Within the U.S., children’s PA was consistently lowest in Georgia/Florida and highest in Washington. Conclusions. Cross-country differences in PA, previously reported for adults and adolescents, are evident in 5-year-old children. In general, PA levels are lower in U.S. children than their European counterparts, and within the U.S., are lower in Georgia/Florida and Colorado than in Washington. Future studies should be designed to identify the factors that explain these differences.

scholarly journals Reexamination of Accelerometer Calibration with Energy Expenditure as Criterion: VO2net Instead of MET for Age-Equivalent Physical Activity Intensity

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3377 ◽  
Author(s):  
Daniel Arvidsson ◽  
Jonatan Fridolfsson ◽  
Christoph Buck ◽  
Örjan Ekblom ◽  
Elin Ekblom-Bak ◽  
...  
Keyword(s):  
Physical Activity ◽  
Age Groups ◽  
Accelerometer Data ◽  
Physical Activity Intensity ◽  
Free Living ◽  
Activity Intensity ◽  
Age Related ◽  
Younger Age ◽  
Metabolic Equivalent Of Task ◽  
Adolescents And Adults

Accelerometer calibration for physical activity (PA) intensity is commonly performed using Metabolic Equivalent of Task (MET) as criterion. However, MET is not an age-equivalent measure of PA intensity, which limits the use of MET-calibrated accelerometers for age-related PA investigations. We investigated calibration using VO2net (VO2gross − VO2stand; mL⋅min−1⋅kg−1) as criterion compared to MET (VO2gross/VO2rest) and the effect on assessment of free-living PA in children, adolescents and adults. Oxygen consumption and hip/thigh accelerometer data were collected during rest, stand and treadmill walk and run. Equivalent speed (Speedeq) was used as indicator of the absolute speed (Speedabs) performed with the same effort in individuals of different body size/age. The results showed that VO2net was higher in younger age-groups for Speedabs, but was similar in the three age-groups for Speedeq. MET was lower in younger age-groups for both Speedabs and Speedeq. The same VO2net-values respective MET-values were applied to all age-groups to develop accelerometer PA intensity cut-points. Free-living moderate-and-vigorous PA was 216, 115, 74 and 71 min/d in children, adolescents, younger and older adults with VO2net-calibration, but 140, 83, 74 and 41 min/d with MET-calibration, respectively. In conclusion, VO2net calibration of accelerometers may provide age-equivalent measures of PA intensity/effort for more accurate age-related investigations of PA in epidemiological research.

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