Comparison of 3 Accelerometer Data Reduction Approaches, Step Counts, and 2 Self-Report Measures for Estimating Physical Activity in Free-Living Adults

2013 ◽  
Vol 10 (7) ◽  
pp. 1068-1074 ◽  
Author(s):  
M. Renée Umstattd Meyer ◽  
Stephanie L. Baller ◽  
Shawn M. Mitchell ◽  
Stewart G. Trost
Keyword(s):  
Physical Activity ◽  
Regression Model ◽  
Data Reduction ◽  
Research Area ◽  
Point Method ◽  
Self Report ◽  
Accelerometer Data ◽  
Free Living ◽  
Cut Point ◽  
Important Research Area

Background:Accelerometers have become one of the most common methods of measuring physical activity (PA). Thus, validity of accelerometer data reduction approaches remains an important research area. Yet, few studies directly compare data reduction approaches and other PA measures in free-living samples.Objective:To compare PA estimates provided by 3 accelerometer data reduction approaches, steps, and 2 self-reported estimates: Crouter’s 2-regression model, Crouter’s refined 2-regression model, the weighted cut-point method adopted in the National Health and Nutrition Examination Survey (NHANES; 2003–2004 and 2005–2006 cycles), steps, IPAQ, and 7-day PA recall.Methods:A worksite sample (N = 87) completed online-surveys and wore ActiGraph GT1M accelerometers and pedometers (SW-200) during waking hours for 7 consecutive days. Daily time spent in sedentary, light, moderate, and vigorous intensity activity and percentage of participants meeting PA recommendations were calculated and compared.Results:Crouter’s 2-regression (161.8 ± 52.3 minutes/day) and refined 2-regression (137.6 ± 40.3 minutes/day) models provided significantly higher estimates of moderate and vigorous PA and proportions of those meeting PA recommendations (91% and 92%, respectively) as compared with the NHANES weighted cut-point method (39.5 ± 20.2 minutes/day, 18%). Differences between other measures were also significant.Conclusions:When comparing 3 accelerometer cut-point methods, steps, and self-report measures, estimates of PA participation vary substantially.

scholarly journals What they say and what they do: comparing physical activity across the USA, England and the Netherlands

2018 ◽  
Vol 72 (6) ◽  
pp. 471-476 ◽  
Author(s):  
Arie Kapteyn ◽  
James Banks ◽  
Mark Hamer ◽  
James P Smith ◽  
Andrew Steptoe ◽  
...  
Keyword(s):  
Physical Activity ◽  
The Netherlands ◽  
Age Groups ◽  
The Self ◽  
Self Report ◽  
Objective Measures ◽  
Accelerometer Data ◽  
Physically Active ◽  
Self Reports ◽  
The Usa

BackgroundPhysical activity (PA) is important for maintaining health, but there are fundamental unanswered questions on how best it should be measured.MethodsWe measured PA in the Netherlands (n=748), the USA (n=540) and England (n=254), both by a 7 day wrist-worn accelerometer and by self-reports. The self-reports included a global self-report on PA and a report on the frequency of vigorous, moderate and mild activity.ResultsThe self-reported data showed only minor differences across countries and across groups within countries (such as different age groups or working vs non-working respondents). The accelerometer data, however, showed large differences; the Dutch and English appeared to be much more physically active than Americans h (For instance, among respondents aged 50 years or older 38% of Americans are in the lowest activity quintile of the Dutch distribution). In addition, accelerometer data showed a sharp decline of PA with age, while no such pattern was observed in self-reports. The differences between objective measures and self-reports occurred for both types of self-reports.ConclusionIt is clear that self-reports and objective measures tell vastly different stories, suggesting that across countries people use different response scales when answering questions about how physically active they are.

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.

scholarly journals Towards a Portable Model to Discriminate Activity Clusters from Accelerometer Data

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4504 ◽  
Author(s):  
Petra Jones ◽  
Evgeny M. Mirkes ◽  
Tom Yates ◽  
Charlotte L. Edwardson ◽  
Mike Catt ◽  
...  
Keyword(s):  
Physical Activity ◽  
Cross Validation ◽  
Accelerometer Data ◽  
Activity Type ◽  
Free Living ◽  
Independent Dataset ◽  
Clustering Model ◽  
Activity Types ◽  
Unsupervised Approach ◽  
Viable Approach

Few methods for classifying physical activity from accelerometer data have been tested using an independent dataset for cross-validation, and even fewer using multiple independent datasets. The aim of this study was to evaluate whether unsupervised machine learning was a viable approach for the development of a reusable clustering model that was generalisable to independent datasets. We used two labelled adult laboratory datasets to generate a k-means clustering model. To assess its generalised application, we applied the stored clustering model to three independent labelled datasets: two laboratory and one free-living. Based on the development labelled data, the ten clusters were collapsed into four activity categories: sedentary, standing/mixed/slow ambulatory, brisk ambulatory, and running. The percentages of each activity type contained in these categories were 89%, 83%, 78%, and 96%, respectively. In the laboratory independent datasets, the consistency of activity types within the clusters dropped, but remained above 70% for the sedentary clusters, and 85% for the running and ambulatory clusters. Acceleration features were similar within each cluster across samples. The clusters created reflected activity types known to be associated with health and were reasonably robust when applied to diverse independent datasets. This suggests that an unsupervised approach is potentially useful for analysing free-living accelerometer data.

scholarly journals Reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease

2021 ◽  
pp. bjsports-2021-104050
Author(s):  
Rosemary Walmsley ◽  
Shing Chan ◽  
Karl Smith-Byrne ◽  
Rema Ramakrishnan ◽  
Mark Woodward ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Sedentary Behaviour ◽  
Lower Risk ◽  
Accelerometer Data ◽  
Uk Biobank ◽  
Free Living ◽  
Machine Learning Methods ◽  
Machine Learning Model ◽  
Incident Cardiovascular Disease

ObjectiveTo improve classification of movement behaviours in free-living accelerometer data using machine-learning methods, and to investigate the association between machine-learned movement behaviours and risk of incident cardiovascular disease (CVD) in adults.MethodsUsing free-living data from 152 participants, we developed a machine-learning model to classify movement behaviours (moderate-to-vigorous physical activity behaviours (MVPA), light physical activity behaviours, sedentary behaviour, sleep) in wrist-worn accelerometer data. Participants in UK Biobank, a prospective cohort, were asked to wear an accelerometer for 7 days, and we applied our machine-learning model to classify their movement behaviours. Using compositional data analysis Cox regression, we investigated how reallocating time between movement behaviours was associated with CVD incidence.ResultsIn leave-one-participant-out analysis, our machine-learning method classified free-living movement behaviours with mean accuracy 88% (95% CI 87% to 89%) and Cohen’s kappa 0.80 (95% CI 0.79 to 0.82). Among 87 498 UK Biobank participants, there were 4105 incident CVD events. Reallocating time from any behaviour to MVPA, or reallocating time from sedentary behaviour to any behaviour, was associated with lower CVD risk. For an average individual, reallocating 20 min/day to MVPA from all other behaviours proportionally was associated with 9% (95% CI 7% to 10%) lower risk, while reallocating 1 hour/day to sedentary behaviour from all other behaviours proportionally was associated with 5% (95% CI 3% to 7%) higher risk.ConclusionMachine-learning methods classified movement behaviours accurately in free-living accelerometer data. Reallocating time from other behaviours to MVPA, and from sedentary behaviour to other behaviours, was associated with lower risk of incident CVD, and should be promoted by interventions and guidelines.

scholarly journals Deriving An Ecologically Valid Accelerometer Cut-point For Free-living Physical Activity In Children: An Exploratory Study

2009 ◽  
Vol 41 ◽  
pp. 172
Author(s):  
Susan Bock ◽  
Christine Steel ◽  
Sally McLure ◽  
Helen Moore ◽  
Daniel Cooley ◽  
...  
Keyword(s):  
Physical Activity ◽  
Exploratory Study ◽  
Free Living ◽  
Cut Point

A pilot study comparing objective physical activity to the physical component of the Eastern Cooperative Oncology Group (ECOG) performance status scale

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e20501-e20501 ◽  
Author(s):  
J. Walsh ◽  
J. Hussey ◽  
D. O'Donnell
Keyword(s):  
Physical Activity ◽  
Good Predictor ◽  
Clinical Decision Making ◽  
Performance Status ◽  
Self Report ◽  
Poor Correlation ◽  
Accelerometer Data ◽  
Ecog Performance Status ◽  
Ps Scale ◽  
Ecog Ps

e20501 Background: The ECOG performance status (PS) scale is widely used in oncology for clinical decision-making, being a good predictor of survival, prognosis, and treatment response. It has never been formally compared to objective measures of physical activity (PA). Accelerometers are now established as detailed valid measures of PA. The aim of this study was to compare the ECOG PS scale to objectively measured PA. Methods: Accelerometer data (RT3) as well as self-report (International Physical Activity Questionnaire (IPAQ)) was collected for at least 3 days immediately before cancer outpatient assessment on 15 patients (6 males, 9 females) with a mean age of 65 years (range 47–51), all with solid tumours. RT3 and self-report data were compared to the ECOG PS score assigned by the treating physician. Time ‘up and about’ is all activity, however minimal, detected by the RT3, except lying flat. Institutional ethics committee approval was gained. Results: Most subjects were assigned an ECOG score of 1. In all subjects, the score assigned over-estimated PA levels ( Table ). Spearman's rank correlation coefficient showed poor correlation between percentage waking time ‘up and about’ and the ECOG PS score assigned (p = -0.1), and between the IPAQ and ECOG scores (p = 0.1). Conclusions: Although ECOG PS is recognized as a good predictor of clinical outcome, a poor correlation was found between ECOG PS assigned, objective PA, and self-report. Subjects were far more sedentary than estimated using physician-assigned ECOG PS. A large study to investigate these relationships further is ongoing. [Table: see text] No significant financial relationships to disclose.

Physical Activity Adoption and Maintenance Among Breast Cancer Survivors: A Randomized Trial of Peer Mentoring

2021 ◽  
Author(s):  
Bernardine M Pinto ◽  
Shira I Dunsiger ◽  
Madison M Kindred ◽  
Sheryl Mitchell
Keyword(s):  
Breast Cancer ◽  
Physical Activity ◽  
Cancer Survivors ◽  
Peer Support ◽  
Text Messaging ◽  
Breast Cancer Survivors ◽  
Self Report ◽  
Phone Call ◽  
Accelerometer Data ◽  
Phone Calls

Abstract Background Peer support can extend the reach of physical activity (PA) interventions. In previous studies, peer support via weekly counseling calls increased PA at 3 and 6 months among breast cancer survivors, compared to contact control. However, effects were attenuated at 6 months. Interventions targeting PA maintenance among cancer survivors are limited. Hence, we extended prior work to identify effective PA maintenance interventions. Purpose Following a 3-month PA intervention, the study compared the effects of three 6-month interventions on PA at 12 months. Methods One hundred and sixty-one inactive breast cancer survivors participated in a 12-month randomized controlled trial. Intervention delivery was uniform for the first 3-months: all participants received a weekly call with their peer coach to encourage PA. Following month 3, participants self-monitored PA and received feedback reports (Reach Plus) or additionally received, a monthly phone call (Reach Plus Phone), or weekly text message (Reach Plus Message). Moderate-to-vigorous PA (MVPA) was measured using self-report (7 Day PAR) and accelerometry at baseline, 3, 6, 9, and 12 months. Results At 3 months, there were significant within group increases in self-reported and objectively measured MVPA with no between-group differences (ps > .05). At 6 months, adjusted longitudinal models showed that Reach Plus Message reported an additional 23.83 (SD = 6.33, f2 = .12) min/week of MVPA and Reach Plus Phone reported an additional 18.14 min/week (SD = 5.15, f2 =.16) versus Reach Plus. Results were similar at 9 months. At 12 months, Reach Plus Message and Reach Plus Phone both out-performed Reach Plus (ps = .04 and .05 respectively and effect sizes f2 = .11 and f2 = .21 respectively). Accelerometer data showed similar patterns: Reach Plus Message and Reach Plus Phone out-performed Reach Plus at 6 (f2 = .20) and 9 months (f2 = .09). Conclusion Phone calls from peer mentors and text messaging can support PA maintenance among breast cancer survivors. Clinical Trial information ClinicalTrials.Gov NCT02694640.

scholarly journals Validity of the Global Physical Activity Questionnaire (GPAQ) in Adult Latinas

2012 ◽  
Vol 9 (5) ◽  
pp. 698-705 ◽  
Author(s):  
Tracy Hoos ◽  
Nancy Espinoza ◽  
Simon Marshall ◽  
Elva M. Arredondo
Keyword(s):  
Physical Activity ◽  
Self Report ◽  
Accelerometer Data ◽  
Post Intervention ◽  
Activity Intensity ◽  
Time Work ◽  
Global Physical Activity Questionnaire ◽  
Work Commute ◽  
The Impact ◽  
Multilevel Intervention

Background:Valid and reliable self-report measures of physical activity (PA) are needed to evaluate the impact of interventions aimed at increasing the levels of PA. However, few valid measures for assessing PA in Latino populations exist.Objective:The purpose of this study is to determine whether the GPAQ is a valid measure of PA among Latinas and to examine its sensitivity to intervention change. Intervention attendance was also examined.Methods:Baseline and postintervention data were collected from 72 Latinas (mean age = 43.01; SD = 9.05) who participated in Caminando con Fe/Walking with Faith, a multilevel intervention promoting PA among church-going Latinas. Participants completed the GPAQ and were asked to wear the accelerometer for 7 consecutive days at baseline and again 6 months later. Accelerometer data were aggregated into 5 levels of activity intensity (sedentary, light, moderate, moderate-vigorous, and vigorous) and correlated to self-reported mean minutes of PA across several domains (leisure time, work, commute and household chores).Results:There were significant correlations at postintervention between self-reported minutes per week of vigorous LTPA and accelerometer measured vigorous PA (r = .404, P < .001) as well as significant correlations of sensitivity to intervention change (post intervention minus baseline) between self-reported vigorous LTPA and accelerometer-measured vigorous PA (r = .383, P < .003) and self-reported total vigorous PA and accelerometer measured vigorous PA (r = .363, P < .003).Conclusions:The findings from this study suggest that the GPAQ may be useful for evaluating the effectiveness of programs aimed at increasing vigorous levels of PA among Latinas.

scholarly journals Ability of RT3 Accelerometer Cut Points to Detect Physical Activity Intensity in Ambulatory Children With Cerebral Palsy

2014 ◽  
Vol 31 (4) ◽  
pp. 310-324 ◽  
Author(s):  
Jennifer Ryan ◽  
Michael Walsh ◽  
John Gormley
Keyword(s):  
Physical Activity ◽  
Cerebral Palsy ◽  
Vigorous Physical Activity ◽  
Accelerometer Data ◽  
Physical Activity Intensity ◽  
Cut Points ◽  
Cut Point ◽  
Activity Intensity ◽  
Metabolic Equivalents ◽  
Children With Cerebral Palsy

This study investigated the ability of published cut points for the RT3 accelerometer to differentiate between levels of physical activity intensity in children with cerebral palsy (CP). Oxygen consumption (metabolic equivalents; METs) and RT3 data (counts/min) were measured during rest and 5 walking trials. METs and corresponding counts/min were classified as sedentary, light physical activity (LPA), and moderate to vigorous physical activity (MVPA) according to MET thresholds. Counts were also classified according to published cut points. A published cut point exhibited an excellent ability to classify sedentary activity (sensitivity = 89.5%, specificity = 100.0%). Classification accuracy decreased when published cut points were used to classify LPA (sensitivity = 88.9%, specificity = 79.6%) and MVPA (sensitivity = 70%, specificity = 95–97%). Derivation of a new cut point improved classification of both LPA and MVPA. Applying published cut points to RT3 accelerometer data collected in children with CP may result in misclassification of LPA and MVPA.

scholarly journals Article Commentary: Improvements in the Measurement of Physical Activity in Childhood Obesity Research; Lessons from Large Studies of Accelerometers

2008 ◽  
Vol 2 ◽  
pp. CMPed.S1127 ◽  
Author(s):  
Calum Mattocks ◽  
Kate Tilling ◽  
Andy Ness ◽  
Chris Riddoch
Keyword(s):  
Physical Activity ◽  
Childhood Obesity ◽  
Children And Adolescents ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
The United States ◽  
Epidemiological Studies ◽  
Self Report ◽  
Free Living ◽  
Chronic Disease Risk

Advances in technology have improved our ability to measure physical activity in free-living humans. In the last few years, several large epidemiological studies in Europe and the United States have used accelerometers to assess physical activity in children and adolescents. The use of accelerometers to study physical activity has presented some challenges on how to summarise and interpret the data that they generate, however these studies are providing important information on the levels and patterns of physical activity among children and adolescents. Some studies have reported that few children and adolescents appear to meet the recommended minimum of 60 minutes of moderate to vigorous activity per day. Accelerometers have also allowed examination of the relationships between physical activity and health outcomes like obesity and other chronic disease risk factors such as insulin resistance, aerobic fitness, blood lipids and blood pressure. Use of accelerometers allows such relationships to be estimated with a precision that was previously impossible with self-report measures of physical activity. Such information is already advancing our understanding of the role that physical activity plays in preventing childhood obesity and cardiovascular disease risk.

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