Standardizing and Optimizing the Use of Accelerometer Data for Free-Living Physical Activity Monitoring

2005 ◽  
Vol 2 (3) ◽  
pp. 366-383 ◽  
Author(s):  
Dale W. Esliger ◽  
Jennifer L. Copeland ◽  
Joel D. Barnes ◽  
Mark S. Tremblay
Keyword(s):  
Public Health ◽  
Physical Activity ◽  
Data Capture ◽  
Accelerometer Data ◽  
Free Living ◽  
Physical Activity Monitoring ◽  
Technological Advances ◽  
Standardized Reporting ◽  
Public Health Officials ◽  
Reporting Procedures

The unequivocal link between physical activity and health has prompted researchers and public health officials to search for valid, reliable, and logistically feasible tools to measure and quantify free-living physical activity. Accelerometers hold promise in this regard. Recent technological advances have led to decreases in both the size and cost of accelerometers while increasing functionality (e.g., greater memory, waterproofing). A lack of common data reduction and standardized reporting procedures dramatically limit their potential, however. The purpose of this article is to expand on the utility of accelerometers for measuring free-living physical activity. A detailed example profile of physical activity is presented to highlight the potential richness of accelerometer data. Specific recommendations for optimizing and standardizing the use of accelerometer data are provided with support from specific examples. This descriptive article is intended to advance and ignite scholarly dialogue and debate regarding accelerometer data capture, reduction, analysis, and reporting.

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 Lessons Learned From the Creating Active Communities and Healthy Environments Toolkit Pilot: A Qualitative Study

2019 ◽  
Vol 13 ◽  
pp. 117863021986223
Author(s):  
Marissa M Shams-White ◽  
Alison Cuccia ◽  
Fernando Ona ◽  
Steven Bullock ◽  
Kenneth Chui ◽  
...  
Keyword(s):  
Public Health ◽  
Physical Activity ◽  
Healthy Eating ◽  
Action Plan ◽  
Implementation Process ◽  
Lessons Learned ◽  
Built Environments ◽  
Free Living ◽  
Military Installations ◽  
Active Communities

The US Army Public Health Center developed the Creating Active Communities and Healthy Environments (CACHE) Toolkit to help military installations evaluate the quality of their built environments relative to healthy eating, physical activity, and tobacco-free living. This study sought to improve its implementation process and assess subsequent Action Plan Guides’ utility at 5 military installations. Baseline data included a knowledge, attitudes, and beliefs survey (N = 34); post-Toolkit implementation data included focus groups (N = 2) and interviews (N = 10). Although >80% of participants agreed the built environment affects healthy living, only 44%, 53%, and 35% agreed their installations’ built environments promoted healthy eating, physical activity, and tobacco-free living, respectively. Emerging themes comprised “Opportunities to Improve Toolkit and Action Plan Guide Functionality,” the “Sociopolitical Landscape Affects Toolkit Implementation,” and the “Sociopolitical and Physical Landscapes Affect the Toolkit’s Value and Utility.” This study provides concrete lessons for the CACHE Toolkit and other public health-based military initiatives.

scholarly journals Impact of Reduced Sampling Rate on Accelerometer-Based Physical Activity Monitoring and Machine Learning Activity Classification

2021 ◽  
pp. 1-13
Author(s):  
Scott Small ◽  
Sara Khalid ◽  
Paula Dhiman ◽  
Shing Chan ◽  
Dan Jackson ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Nearest Neighbor ◽  
Sampling Rate ◽  
Learning Activity ◽  
Activity Measurement ◽  
Battery Life ◽  
Accelerometer Data ◽  
Wear Time ◽  
Physical Activity Monitoring

Purpose: Lowering the sampling rate of accelerometers in physical activity research can dramatically increase study monitoring periods through longer battery life; however, the effect of reduced sampling rate on activity metric validity is poorly documented. We therefore aimed to assess the effect of reduced sampling rate on measuring physical activity both overall and by specific behavior types. Methods: Healthy adults wore sets of two Axivity AX3 accelerometers on the dominant wrist and hip for 24 hr. At each location one accelerometer recorded at 25 Hz and the other at 100 Hz. Overall acceleration magnitude, time in moderate to vigorous activity, and behavioral activities were calculated and processed using both linear and nearest neighbor resampling. Correlation between acceleration magnitude and activity classifications at both sampling rates was calculated and linear regression was performed. Results: Of the 54 total participants, 45 contributed >20 hr of hip wear time and 51 contributed >20 hr of wrist wear time. Strong correlation was observed between 25- and 100-Hz sampling rates in overall activity measurement (r = .97–.99), yet consistently lower activity was observed in data collected at 25 Hz (3.1%–13.9%). Reduced sleep and light activity and increased sedentary time was classified in 25-Hz data by machine learning models. Discrepancies were greater when linear interpolation resampling was used in postprocessing. Conclusions: The 25- and 100-Hz accelerometer data are highly correlated with predictable differences, which can be accounted for in interstudy comparisons. Sampling rate and resampling methods should be consistently reported in physical activity studies, carefully considered in study design, and tailored to the outcome of interest.

scholarly journals The MIPAM trial: a 12-week intervention with motivational interviewing and physical activity monitoring to enhance the daily amount of physical activity in community-dwelling older adults – a study protocol for a randomized controlled trial

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Rasmus Tolstrup Larsen ◽  
Christoffer Bruun Korfitsen ◽  
Carsten Bogh Juhl ◽  
Henning Boje Andersen ◽  
Jan Christensen ◽  
...  
Keyword(s):  
Public Health ◽  
Physical Activity ◽  
Older Adults ◽  
Motivational Interviewing ◽  
Self Efficacy ◽  
Community Dwelling ◽  
World Health ◽  
Secondary Outcome ◽  
Randomized Controlled ◽  
Physical Activity Monitoring

Abstract Background Physical Activity Monitors (PAMs) have been shown to effectively enhance level of physical activity (PA) in older adults. Motivational interviewing is a person-centred model where participants are guided using self-reflection and counselling, and addresses the behavioural and psychological aspects of why people initiate health behaviour change by prompting increases in motivation and self-efficacy. The addition of motivational interviewing to PA interventions may increase the effectiveness of PAMs for older adults. Methods This motivational interviewing and PA monitoring trial is designed as an investigator-blinded, two arm parallel group, randomized controlled superiority trial with primary endpoint after 12 weeks of intervention. The intervention group will receive a PAM-based intervention and motivational interviewing and the control group will only receive the PAM-based intervention. The primary outcome is PA, objectively measured as the average daily number of steps throughout the intervention period. Secondary outcome measures include self-reported PA health-related quality of life, loneliness, self-efficacy for exercise, outcome expectancy for exercise, and social relations. The outcomes will be analysed with a linear regression model investigating between-group differences, adjusted for baseline scores. Following the intention to treat principle, multiple imputation will be performed to handle missing values. Discussion A moderate effect of daily PA measured using PAMs is expected in this superiority RCT investigating the effect of adding motivational interviewing to a PAM intervention. According to the World Health Organization, walking and cycling are key activities in regular PA and should be promoted. To increase the general public health and lower the burden of inactivity in older adults, cost-beneficial solutions should be investigated further. If this RCT shows that motivational interviewing can enhance the effect of PAM-based interventions, it might be included as an add-on intervention when appropriate. No matter what the results of this study will be, the conclusions will be relevant for clinicians as the dependence on technology is increasing, especially in relation to public health promotion. Trial registration NCT03906162, April 1, 2019.

scholarly journals Machine Learning to Quantify Physical Activity in Children with Cerebral Palsy: Comparison of Group, Group-Personalized, and Fully-Personalized Activity Classification Models

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3976
Author(s):  
Matthew N. Ahmadi ◽  
Margaret E. O’Neil ◽  
Emmah Baque ◽  
Roslyn N. Boyd ◽  
Stewart G. Trost
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Cerebral Palsy ◽  
Significant Heterogeneity ◽  
Accelerometer Data ◽  
Free Living ◽  
Children With Cerebral Palsy ◽  
Comparative Accuracy ◽  
Free Living Conditions ◽  
Gmfcs Level

Pattern recognition methodologies, such as those utilizing machine learning (ML) approaches, have the potential to improve the accuracy and versatility of accelerometer-based assessments of physical activity (PA). Children with cerebral palsy (CP) exhibit significant heterogeneity in relation to impairment and activity limitations; however, studies conducted to date have implemented “one-size fits all” group (G) models. Group-personalized (GP) models specific to the Gross Motor Function Classification (GMFCS) level and fully-personalized (FP) models trained on individual data may provide more accurate assessments of PA; however, these approaches have not been investigated in children with CP. In this study, 38 children classified at GMFCS I to III completed laboratory trials and a simulated free-living protocol while wearing an ActiGraph GT3X+ on the wrist, hip, and ankle. Activities were classified as sedentary, standing utilitarian movements, or walking. In the cross-validation, FP random forest classifiers (99.0–99.3%) exhibited a significantly higher accuracy than G (80.9–94.7%) and GP classifiers (78.7–94.1%), with the largest differential observed in children at GMFCS III. When evaluated under free-living conditions, all model types exhibited significant declines in accuracy, with FP models outperforming G and GP models in GMFCS levels I and II, but not III. Future studies should evaluate the comparative accuracy of personalized models trained on free-living accelerometer data.

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