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Hodkinson A, Kontopantelis E, Zghebi SS, Grigoroglou C, McMillan B, Marwijk HV, Bower P, Tsimpida D, Emery CF, Burge MR, Esmiol H, Cupples ME, Tully MA, Dasgupta K, Daskalopoulou SS, Cooke AB, Fayehun AF, Houle J, Poirier P, Yates T, Henson J, Anderson DR, Grey EB, Panagioti M. Association Between Patient Factors and the Effectiveness of Wearable Trackers at Increasing the Number of Steps per Day Among Adults With Cardiometabolic Conditions: Meta-analysis of Individual Patient Data From Randomized Controlled Trials. J Med Internet Res 2022; 24:e36337. [PMID: 36040779 PMCID: PMC9472038 DOI: 10.2196/36337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/14/2022] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Current evidence supports the use of wearable trackers by people with cardiometabolic conditions. However, as the health benefits are small and confounded by heterogeneity, there remains uncertainty as to which patient groups are most helped by wearable trackers. OBJECTIVE This study examined the effects of wearable trackers in patients with cardiometabolic conditions to identify subgroups of patients who most benefited and to understand interventional differences. METHODS We obtained individual participant data from randomized controlled trials of wearable trackers that were conducted before December 2020 and measured steps per day as the primary outcome in participants with cardiometabolic conditions including diabetes, overweight or obesity, and cardiovascular disease. We used statistical models to account for clustering of participants within trials and heterogeneity across trials to estimate mean differences with the 95% CI. RESULTS Individual participant data were obtained from 9 of 25 eligible randomized controlled trials, which included 1481 of 3178 (47%) total participants. The wearable trackers revealed that over the median duration of 12 weeks, steps per day increased by 1656 (95% CI 918-2395), a significant change. Greater increases in steps per day from interventions using wearable trackers were observed in men (interaction coefficient -668, 95% CI -1157 to -180), patients in age categories over 50 years (50-59 years: interaction coefficient 1175, 95% CI 377-1973; 60-69 years: interaction coefficient 981, 95% CI 222-1740; 70-90 years: interaction coefficient 1060, 95% CI 200-1920), White patients (interaction coefficient 995, 95% CI 360-1631), and patients with fewer comorbidities (interaction coefficient -517, 95% CI -1188 to -11) compared to women, those aged below 50, non-White patients, and patients with multimorbidity. In terms of interventional differences, only face-to-face delivery of the tracker impacted the effectiveness of the interventions by increasing steps per day. CONCLUSIONS In patients with cardiometabolic conditions, interventions using wearable trackers to improve steps per day mostly benefited older White men without multimorbidity. TRIAL REGISTRATION PROSPERO CRD42019143012; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=143012.
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Affiliation(s)
- Alexander Hodkinson
- Division of Population Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.,Health Services Research and Primary Care, National Institute for Health Research School for Primary Care Research, Manchester, United Kingdom
| | - Evangelos Kontopantelis
- Division of Population Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.,Health Services Research and Primary Care, National Institute for Health Research School for Primary Care Research, Manchester, United Kingdom.,Division of Informatics, Imaging & Data Sciences, Faculty of Biology, Medicine & Health, University of Manchester, Manchester, United Kingdom
| | - Salwa S Zghebi
- Division of Population Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.,Health Services Research and Primary Care, National Institute for Health Research School for Primary Care Research, Manchester, United Kingdom
| | - Christos Grigoroglou
- Division of Population Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.,Health Services Research and Primary Care, National Institute for Health Research School for Primary Care Research, Manchester, United Kingdom
| | - Brian McMillan
- Division of Population Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.,Health Services Research and Primary Care, National Institute for Health Research School for Primary Care Research, Manchester, United Kingdom
| | - Harm van Marwijk
- Department of Primary Care and Public Health, Brighton and Sussex Medical School, University of Brighton, Brighton, United Kingdom
| | - Peter Bower
- Division of Population Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.,Health Services Research and Primary Care, National Institute for Health Research School for Primary Care Research, Manchester, United Kingdom
| | - Dialechti Tsimpida
- Division of Population Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.,Health Services Research and Primary Care, National Institute for Health Research School for Primary Care Research, Manchester, United Kingdom
| | - Charles F Emery
- Department of Psychology, The Ohio State University College of Arts and Sciences, Columbus, OH, United States
| | - Mark R Burge
- Department of Medicine, Endocrinology and Metabolism, University of New Mexico Health Sciences Center, Albuquerque, NM, United States
| | - Hunter Esmiol
- Department of Medicine, Endocrinology and Metabolism, University of New Mexico Health Sciences Center, Albuquerque, NM, United States
| | - Margaret E Cupples
- Department of General Practice and Primary Care, Centre for Public Heath, Queen's University Belfast, Belfast, United Kingdom
| | - Mark A Tully
- School of Medicine, Ulster University, Londonderry, United Kingdom
| | - Kaberi Dasgupta
- Department of Medicine, McGill University, Montreal, QC, Canada.,Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Stella S Daskalopoulou
- Department of Medicine, McGill University, Montreal, QC, Canada.,Centre for Translational Biology, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | | | - Ayorinde F Fayehun
- Department of Family Medicine, University College Hospital, Ibadan, Nigeria
| | - Julie Houle
- Department of Nursing, Université du Québec à Trois-Rivières, Trois-Rivières, QC, Canada
| | - Paul Poirier
- Institut Universitaire de Cardiologie et de Pneumologie de Québec, Université Laval, Laval, QC, Canada
| | - Thomas Yates
- Diabetes Research Centre, University of Leicester, Leicester, United Kingdom
| | - Joseph Henson
- Diabetes Research Centre, University of Leicester, Leicester, United Kingdom
| | - Derek R Anderson
- Department of Psychology, The Ohio State University College of Arts and Sciences, Columbus, OH, United States
| | - Elisabeth B Grey
- Centre for Motivation and Health Behaviour Change, Department for Health, University of Bath, Bath, United Kingdom
| | - Maria Panagioti
- Division of Population Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.,Health Services Research and Primary Care, National Institute for Health Research School for Primary Care Research, Manchester, United Kingdom
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Ten Velde G, Plasqui G, Dorenbos E, Winkens B, Vreugdenhil A. Objectively measured physical activity and sedentary time in children with overweight, obesity and morbid obesity: a cross-sectional analysis. BMC Public Health 2021; 21:1558. [PMID: 34404361 PMCID: PMC8369633 DOI: 10.1186/s12889-021-11555-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 07/27/2021] [Indexed: 11/10/2022] Open
Abstract
Background Limited physical activity (PA) and a high level of sedentary time (ST) are associated with childhood obesity and are a target for intervention. This study aimed to assess objectively measured PA and ST in Dutch children across weight categories, age groups and sex. Methods 202 children with overweight, obesity and morbid obesity (55% girls, 12 ± 3y of age, BMI z-score + 3.15 ± 0.73), referred to the Centre for Overweight Adolescents and Children’s Healthcare (COACH, Maastricht UMC+) were included. PA (total PA, light PA and moderate to vigorous PA (MVPA)) and ST were measured with the GT3X Actigraph accelerometer. Wear time validation was set to include at least four days, 480 min/day, including one weekend day. Results PA levels in children with morbid obesity were higher compared to children with obesity, also after correction for age and sex (corrected difference (B) 118 counts per minute (cpm), p = .006). ST was lower in children with morbid obesity compared to children with obesity (B − 51 min/day, p = .018). Girls performed significantly less MVPA than boys (B − 11 min/day, p < .001) and for each year increase of age, children performed less PA (B − 46 cpm, p < .001) and ST increased (B 18 min/day, p < .001). Conclusion PA and ST is different in subgroups of children with overweight, obesity and morbid obesity, depending on sex, age and overweight severity. In particular, children with obesity perform less PA and more ST compared to children with morbid obesity. Future research could explore the preferences and needs for PA and ST in children in the different weight categories. Trial registration The trial is registered with Clinicaltrials.govNCT02091544 at March 19, 2014. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-021-11555-5.
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Affiliation(s)
- Gabrielle Ten Velde
- Centre for Overweight Adolescent and Children's Healthcare (COACH), Maastricht University Medical Centre, P.O. Box 5800, 6202, AZ, Maastricht, the Netherlands. .,Department of Nutrition and Movement Scienes, Maastricht University, P.O. Box 616, 6200, MD, Maastricht, The Netherlands.
| | - Guy Plasqui
- Department of Nutrition and Movement Scienes, Maastricht University, P.O. Box 616, 6200, MD, Maastricht, The Netherlands
| | - Elke Dorenbos
- Centre for Overweight Adolescent and Children's Healthcare (COACH), Maastricht University Medical Centre, P.O. Box 5800, 6202, AZ, Maastricht, the Netherlands.,Department of Nutrition and Movement Scienes, Maastricht University, P.O. Box 616, 6200, MD, Maastricht, The Netherlands
| | - Bjorn Winkens
- Department of Methodology and Statistics, Care And Public Health Research Institute (CAPHRI), Maastricht University, P.O. Box 616, 6200, MD, Maastricht, The Netherlands
| | - Anita Vreugdenhil
- Centre for Overweight Adolescent and Children's Healthcare (COACH), Maastricht University Medical Centre, P.O. Box 5800, 6202, AZ, Maastricht, the Netherlands.,Department of Nutrition and Movement Scienes, Maastricht University, P.O. Box 616, 6200, MD, Maastricht, The Netherlands
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3
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Diniz-Sousa F, Veras L, Ribeiro JC, Boppre G, Devezas V, Santos-Sousa H, Preto J, Machado L, Vilas-Boas JP, Oliveira J, Fonseca H. Accelerometry calibration in people with class II-III obesity: Energy expenditure prediction and physical activity intensity identification. Gait Posture 2020; 76:104-109. [PMID: 31756665 DOI: 10.1016/j.gaitpost.2019.11.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Revised: 10/19/2019] [Accepted: 11/09/2019] [Indexed: 02/02/2023]
Abstract
BACKGROUND Almost all accelerometer calibration studies were developed for non-obese people, which hampers an accurate prediction of energy expenditure (EE) and induces a misclassification of sedentary activity (SA) and physical activity intensities (PAI) in class II-III obese people. RESEARCH QUESTION The purpose of this study was to develop regression equations to predict EE and cut-points to classify SA and PAI in severe obese people based on several metrics obtained from hip and back accelerometer placement data. METHODS 43 class II-III obese participants performed a protocol that included sitting and standing positions and walking at several speeds. During the protocol participants wore an accelerometer at hip and back, and respiratory gas exchange was measured by indirect calorimetry. Accelerometer metrics analyzed were: activity counts, mean amplitude deviation and euclidean norm minus one. EE was predicted through linear mixed models while cut-points to classify SA and PAI were obtained applying receiver operating characteristic curves. Leave-one-out cross-validation data was used to calculate Bland-Altman plots, prediction accuracy, Kappa statistic and percent agreement. RESULTS All prediction models presented a quadratic equation that had as predictors body mass and one of the accelerometer metrics. Predicted EE indicated a good agreement and a root mean square error below 1.02 kcal min-1. Global classification agreement from developed cut-points was categorized as almost perfect with a percent agreement above 84 %. Prediction accuracy and classification agreement were similar among accelerometer metrics in each position and between them in hip and back placement. SIGNIFICANCE Hip and back accelerometer data collected in severe obese people allow to accurately estimate EE and to correctly classify SA and PAI. These results enable future studies to adopt appropriate regression equations and cut-points developed for class II-III obese people rather than those established for non-obese people.
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Affiliation(s)
- Florêncio Diniz-Sousa
- Research Center in Physical Activity, Health and Leisure (CIAFEL), Faculty of Sport, University of Porto, Porto, Portugal.
| | - Lucas Veras
- Research Center in Physical Activity, Health and Leisure (CIAFEL), Faculty of Sport, University of Porto, Porto, Portugal
| | - José Carlos Ribeiro
- Research Center in Physical Activity, Health and Leisure (CIAFEL), Faculty of Sport, University of Porto, Porto, Portugal
| | - Giorjines Boppre
- Research Center in Physical Activity, Health and Leisure (CIAFEL), Faculty of Sport, University of Porto, Porto, Portugal
| | - Vítor Devezas
- General Surgery Department, São João Medical Center, Porto, Portugal
| | - Hugo Santos-Sousa
- General Surgery Department, São João Medical Center, Porto, Portugal
| | - John Preto
- General Surgery Department, São João Medical Center, Porto, Portugal
| | - Leandro Machado
- Center of Research, Education, Innovation and Intervention in Sport (CIFI2D), Faculty of Sport, University of Porto, Porto, Portugal; Biomechanics Laboratory (LABIOMEP-UP), University of Porto, Porto, Portugal
| | - João Paulo Vilas-Boas
- Center of Research, Education, Innovation and Intervention in Sport (CIFI2D), Faculty of Sport, University of Porto, Porto, Portugal; Biomechanics Laboratory (LABIOMEP-UP), University of Porto, Porto, Portugal
| | - José Oliveira
- Research Center in Physical Activity, Health and Leisure (CIAFEL), Faculty of Sport, University of Porto, Porto, Portugal
| | - Hélder Fonseca
- Research Center in Physical Activity, Health and Leisure (CIAFEL), Faculty of Sport, University of Porto, Porto, Portugal; General Surgery Department, São João Medical Center, Porto, Portugal
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Allred AT, Choi P, Agiovlasitis S. Triaxial accelerometer output predicts oxygen uptake in adults with Down syndrome. Disabil Rehabil 2019; 43:2602-2609. [PMID: 31880164 DOI: 10.1080/09638288.2019.1706648] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
PURPOSE This study examined if the relationship between the rate of oxygen uptake (V̇O2) and output from hip-and wrist-worn accelerometers differs between adults with and without DS, and evaluated the accuracy of accelerometer output in estimating V̇O2. MATERIALS AND METHODS Sixteen adults with DS (10 men) and 19 adults without DS (10 men) performed 12 tasks including physical activities and sedentary behaviors. We measured V̇O2 with portable spirometry and accelerometer output (vector magnitude [VM]) with hip- and wrist-worn accelerometers. We used multi-level regressions to predict V̇O2 from VM, group, body mass index (BMI), age, height, weight, and sex. We evaluated prediction accuracy with absolute percent error and Bland-Altman plots. RESULTS For both hip- and wrist-accelerometers, VM and group significantly predicted V̇O2 (p ≤ 0.021). When BMI was added, BMI was a significant predictor but group was not. The final models included VM and BMI (p ≤ 0.001; R2 = 0.78 and 0.57, for hip and wrist accelerometer model, respectively). Absolute error was greater for the wrist- than the hip-accelerometer model (wrist: 37.9 ± 38.1%; hip: 22.5 ± 27.4%). CONCLUSIONS Adults with DS have different V̇O2 to VM responses, and this appears due to their higher BMI. Predictability of V̇O2 from accelerometer output is better for hip- than wrist-worn accelerometers.IMPLICATIONS FOR REHABILITATIONOutput from a triaxial accelerometer has high potential in predicting the energy expenditure and classifying the intensity of physical activity and sedentary behavior in adults with Down syndrome.Accuracy of predicting energy expenditure from accelerometer output is better for hip- than wrist-worn triaxial accelerometers.The development of appropriate rehabilitation interventions that include physical activity for improving health and function in adults with Down syndrome requires accurate assessments of physical activity levels.
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Affiliation(s)
- Anthony T Allred
- Department of Kinesiology, Mississippi State University, Mississippi State, MS, USA
| | - Poram Choi
- Department of Kinesiology, Mississippi State University, Mississippi State, MS, USA
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5
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Stewart T, Narayanan A, Hedayatrad L, Neville J, Mackay L, Duncan S. A Dual-Accelerometer System for Classifying Physical Activity in Children and Adults. Med Sci Sports Exerc 2019; 50:2595-2602. [PMID: 30048411 DOI: 10.1249/mss.0000000000001717] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
INTRODUCTION Accurately monitoring 24-h movement behaviors is a vital step for progressing the time-use epidemiology field. Past accelerometer-based measurement protocols are either hindered by lack of wear time compliance, or the inability to accurately discern activities and postures. Recent work has indicated that skin-attached dual-accelerometers exhibit excellent 24-h uninterrupted wear time compliance. This study extends this work by validating this system for classifying various physical activities and sedentary behaviors in children and adults. METHODS Seventy-five participants (42 children) were equipped with two Axivity AX3 accelerometers; one attached to their thigh, and one to their lower back. Ten activity trials (e.g., sitting, standing, lying, walking, running) were performed while under direct observation in a lab setting. Various time- and frequency-domain features were computed from raw accelerometer data, which were then used to train a random forest machine learning classifier. Model performance was evaluated using leave-one-out cross-validation. The efficacy of the dual-sensor protocol (relative to single sensors) was evaluated by repeating the modeling process with each sensor individually. RESULTS Machine learning models were able to differentiate between six distinct activity classes with exceptionally high accuracy in both adults (99.1%) and children (97.3%). When a single thigh or back accelerometer was used, there was a pronounced drop in accuracy for nonambulatory activities (up to a 26.4% decline). When examining the features used for model training, those that took the orientation of both sensors into account concurrently were more important predictors. CONCLUSIONS When previous wear time compliance results are taken together with our findings, it represents a promising step forward for monitoring and understanding 24-h time-use behaviors. The next step will be to examine the generalizability of these findings in a free-living setting.
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Affiliation(s)
- Tom Stewart
- School of Sport and Recreation, Auckland University of Technology, Auckland, NEW ZEALAND
| | - Anantha Narayanan
- School of Sport and Recreation, Auckland University of Technology, Auckland, NEW ZEALAND
| | - Leila Hedayatrad
- School of Sport and Recreation, Auckland University of Technology, Auckland, NEW ZEALAND
| | - Jonathon Neville
- School of Sport and Recreation, Auckland University of Technology, Auckland, NEW ZEALAND.,School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, NEW ZEALAND
| | - Lisa Mackay
- School of Sport and Recreation, Auckland University of Technology, Auckland, NEW ZEALAND
| | - Scott Duncan
- School of Sport and Recreation, Auckland University of Technology, Auckland, NEW ZEALAND
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Looney DP, Potter AW, Pryor JL, Bremner PE, Chalmers CR, McClung HL, Welles AP, Santee WR. Metabolic Costs of Standing and Walking in Healthy Military-Age Adults: A Meta-regression. Med Sci Sports Exerc 2019; 51:346-351. [PMID: 30649093 DOI: 10.1249/mss.0000000000001779] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
INTRODUCTION The Load Carriage Decision Aid (LCDA) is a U.S. Army planning tool that predicts physiological responses of soldiers during different dismounted troop scenarios. We aimed to develop an equation that calculates standing and walking metabolic rates in healthy military-age adults for the LCDA using a meta-regression. METHODS We searched for studies that measured the energetic cost of standing and treadmill walking in healthy men and women via indirect calorimetry. We used mixed effects meta-regression to determine an optimal equation to calculate standing and walking metabolic rates as a function of walking speed (S, m·s). The optimal equation was used to determine the economical speed at which the metabolic cost per distance walked is minimized. The estimation precision of the new LCDA walking equation was compared with that of seven reference predictive equations. RESULTS The meta-regression included 48 studies. The optimal equation for calculating normal standing and walking metabolic rates (W·kg) was 1.44 + 1.94S + 0.24S. The economical speed for level walking was 1.39 m·s (~ 3.1 mph). The LCDA walking equation was more precise across all walking speeds (bias ± SD, 0.01 ± 0.33 W·kg) than the reference predictive equations. CONCLUSION Practitioners can use the new LCDA walking equation to calculate energy expenditure during standing and walking at speeds <2 m·s in healthy, military-age adults. The LCDA walking equation avoids the errors estimated by other equations at lower and higher walking speeds.
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Affiliation(s)
- David P Looney
- U.S. Army Research Institute of Environmental Medicine (USARIEM), Natick, MA
| | - Adam W Potter
- U.S. Army Research Institute of Environmental Medicine (USARIEM), Natick, MA
| | - J Luke Pryor
- Department of Kinesiology, California State University, Fresno, CA
| | - Patricia E Bremner
- Alvin O. Ramsley Technical Library, U.S. Army Natick Soldier Research, Development and Engineering Center, Natick, MA
| | - Christopher R Chalmers
- U.S. Army Research Institute of Environmental Medicine (USARIEM), Natick, MA.,Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN
| | - Holly L McClung
- U.S. Army Research Institute of Environmental Medicine (USARIEM), Natick, MA
| | - Alexander P Welles
- U.S. Army Research Institute of Environmental Medicine (USARIEM), Natick, MA
| | - William R Santee
- U.S. Army Research Institute of Environmental Medicine (USARIEM), Natick, MA.,Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN
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Abstract
Step counting has long been used as a method of measuring distance. Starting in the mid-1900s, researchers became interested in using steps per day to quantify ambulatory physical activity. This line of research gained momentum after 1995, with the introduction of reasonably accurate spring-levered pedometers with digital displays. Since 2010, the use of accelerometer-based “activity trackers” by private citizens has skyrocketed. Steps have several advantages as a metric for assessing physical activity: they are intuitive, easy to measure, objective, and they represent a fundamental unit of human ambulatory activity. However, since they measure a human behavior, they have inherent biological variability; this means that measurements must be made over 3–7 days to attain valid and reliable estimates. There are many different kinds of step counters, designed to be worn on various sites on the body; all of these devices have strengths and limitations. In cross-sectional studies, strong associations between steps per day and health variables have been documented. Currently, at least eight prospective, longitudinal studies using accelerometers are being conducted that may help to establish dose–response relationships between steps/day and health outcomes. Longitudinal interventions using step counters have shown that they can help inactive individuals to increase by 2500 steps per day. Step counting is useful for surveillance, and studies have been conducted in a number of countries around the world. Future challenges include the need to establish testing protocols and accuracy standards, and to decide upon the best placement sites. These challenges should be addressed in order to achieve harmonization between studies, and to accurately quantify dose–response relationships.
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Assessing Physical Activity as a Core Component in Cardiac Rehabilitation: A POSITION STATEMENT OF THE AMERICAN ASSOCIATION OF CARDIOVASCULAR AND PULMONARY REHABILITATION. J Cardiopulm Rehabil Prev 2017; 36:217-29. [PMID: 27307067 DOI: 10.1097/hcr.0000000000000191] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Physical inactivity is a well-established major risk factor for cardiovascular disease. As such, physical activity counseling is 1 of the 10 core components of cardiac rehabilitation/secondary prevention programs recommended by the American Heart Association and the American Association of Cardiovascular and Pulmonary Rehabilitation (AACVPR). In addition, the ability to perform a physical activity assessment and report outcomes is 1 of the 10 core competencies of cardiac rehabilitation/secondary prevention professionals published by the AACVPR. Unfortunately, standardized procedures for physical activity assessment of cardiac rehabilitation patients have not been developed and published. Thus, the objective of this AACVPR statement is to provide an overview of physical activity assessment concepts and procedures and to provide a recommended approach for performing a standardized assessment of physical activity in all comprehensive cardiac rehabilitation programs following the core components recommendations.
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Mitchell T, Borner K, Finch J, Kerr J, Carlson JA. Using Activity Monitors to Measure Sit-to-Stand Transitions in Overweight/Obese Youth. Med Sci Sports Exerc 2017; 49:1592-1598. [PMID: 28288011 DOI: 10.1249/mss.0000000000001266] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
INTRODUCTION Reducing sedentary behavior has emerged as an important health intervention strategy. Although hip-worn, and more recently wrist-worn, accelerometers are commonly used for measuring physical activity and sedentary behavior, they may not provide accurate measures of postural changes. The current study examined the validity of commonly used hip- and wrist-worn accelerometer cut points and the thigh-worn activPAL activity monitor for measuring sit-to-stand transitions as compared with direct observation in youth with overweight and obesity. METHODS Nine children wore three activity monitors while being directly observed. The monitors included a hip- and wrist-worn ActiGraph and thigh-worn activPAL. The hip-worn ActiGraph was processed with the normal- and low-frequency filters and the inclinometer function. Cut points of ≤25 counts per 15-s epoch for the hip and ≤105 counts per 15-s epoch for the wrist were applied to the vertical axis to identify sit-to-stand transitions. Epoch-level absolute agreement, Bland-Altman plots, mixed-effects linear regression, and intraclass correlation coefficients (ICC) were investigated. RESULTS The hip and wrist accelerometer cut points and the hip inclinometer function overestimated the number of hourly sit-to-stand transitions by approximately triple as compared with direct observation. ICC values between the ActiGraph methods and the direct observation were all <0.12. Sit-to-stand transitions assessed from activPAL were within 17% of direct observation; ICC was 0.26. CONCLUSION Despite the common use of the 100-count hip-worn accelerometer cut point for assessing sedentary time, these processing decisions should be used with caution for assessing sit-to-stand transitions. Future research should investigate other processing methods for ActiGraph data, and studies investigating postural changes should consider including devices such as activPAL.
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Affiliation(s)
- Tarrah Mitchell
- 1University of Kansas, Lawrence, KS; 2Children's Mercy Hospital, Kansas City, MO; 3University of California San Diego, La Jolla, CA; and 4University of Missouri Kansas City, Kansas City, MO
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10
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Clarke-Cornwell AM, Farragher TM, Cook PA, Granat MH. Empirically derived cut-points for sedentary behaviour: are we sitting differently? Physiol Meas 2016; 37:1669-1685. [PMID: 27652920 DOI: 10.1088/0967-3334/37/10/1669] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Sedentary behaviour (SB) is associated with a number of adverse health outcomes. Studies that have used ActiGraph monitors to define sedentary time tend to use a threshold of <100 counts per minute (cpm) for classifying SB; however, this cut-point was not empirically derived for adults. It is not known whether ActiGraph cut-points for SB differ depending on the context in which it occurs. We aimed to: (1) empirically derive an optimal threshold for classifying SB, using the cpm output from the ActiGraph GT3X+, compared to the sedentary classification from the activPAL3™; and (2) ascertain whether this varied by day of the week and in working time versus non-working time. A convenience sample of 30 office-based university employees (females (66.67%); age 40.47 ± 10.95 years; BMI 23.93 ± 2.46 kg m-2) wore the ActiGraph GT3X+ and activPAL3™ devices simultaneously for seven days. Data were downloaded in 1 min epochs and non-wear time was removed. Generalised estimating equations were used to make minute by minute comparisons of sedentary time from the two devices, using sedentary minutes (when all 60 s were classified as sitting/lying) from the activPAL3™ as the criterion measure. After data reduction participants provided on average 11 h 27 min of data per day. The derived cut-points from the models were significantly higher on a Saturday (97 cpm) compared to weekdays (60 cpm) and Sunday (57 cpm). Derived cpm for sedentary time during working time were significantly lower compared to non-working time (35 (95%CI 30-41) versus 73 (54-113)). Compared to the 100 cpm and 150 cpm thresholds, the empirically derived cut-points were not significantly different in terms of area-under-the-curve, but had lower mean bias for working and non-working times. Accelerometer cut-points for SB can depend on day and also domain, suggesting that the nature of sitting differs depending on the context in which sedentary time is accrued.
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Johansson E, Larisch LM, Marcus C, Hagströmer M. Calibration and Validation of a Wrist- and Hip-Worn Actigraph Accelerometer in 4-Year-Old Children. PLoS One 2016; 11:e0162436. [PMID: 27617962 PMCID: PMC5019366 DOI: 10.1371/journal.pone.0162436] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Accepted: 08/23/2016] [Indexed: 11/19/2022] Open
Abstract
Introduction To determine time spent at different physical activity intensities, accelerometers need calibration. The aim of this study was to develop and cross-validate intensity thresholds for the Actigraph GT3X+ accelerometer for wrist and hip placement in four-year-old children. Methods In total 30 children (49 months, SD 3.7) were recruited from five preschools in Stockholm. Equipped with an accelerometer on the wrist and another on the hip, children performed three indoor activities and one free-play session while being video recorded. Subsequently, physical activity intensity levels were coded every 5th second according to the Children’s Activity Rating Scale. Receiver Operating Characteristic (ROC) curves was used to develop wrist and hip intensity thresholds, the upper threshold for sedentary, and lower threshold for moderate-to-vigorous physical activity (MVPA), for the vertical axis (VA) and for the vector magnitude (VM). A leave-one-out method was used to cross-validate the thresholds. Results Intensity thresholds for wrist placement were ≤ 178 (VA) and ≤ 328 (VM) for sedentary and ≥ 871 (VA) and ≥ 1393 (VM) counts/5 seconds for MVPA. The corresponding thresholds for hip placement were ≤ 43 (VA) and ≤ 105 (VM) for sedentary and ≥ 290 (VA) and ≥ 512 (VM) for MVPA. The quadratic weighted Kappa was 0.92 (95% CI 0.91–0.93) (VA) and 0.95 (95% CI 0.94–0.96) (VM) for the wrist-worn accelerometer and 0.76 (98% CI 0.74–0.77) and 0.86 (95% CI 0.85–0.87) for the hip-worn. Conclusion Using wrist placement and the VM when measuring physical activity with accelerometry in 4-year-old children is recommended.
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Affiliation(s)
- Elin Johansson
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- * E-mail:
| | - Lisa-Marie Larisch
- Department of Clinical Science, Technology and Intervention, Karolinska Institutet, Stockholm, Sweden
| | - Claude Marcus
- Department of Clinical Science, Technology and Intervention, Karolinska Institutet, Stockholm, Sweden
| | - Maria Hagströmer
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
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Correa JB, Apolzan JW, Shepard DN, Heil DP, Rood JC, Martin CK. Evaluation of the ability of three physical activity monitors to predict weight change and estimate energy expenditure. Appl Physiol Nutr Metab 2016; 41:758-66. [PMID: 27270210 DOI: 10.1139/apnm-2015-0461] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Activity monitors such as the Actical accelerometer, the Sensewear armband, and the Intelligent Device for Energy Expenditure and Activity (IDEEA) are commonly validated against gold standards (e.g., doubly labeled water, or DLW) to determine whether they accurately measure total daily energy expenditure (TEE) or activity energy expenditure (AEE). However, little research has assessed whether these parameters or others (e.g., posture allocation) predict body weight change over time. The aims of this study were to (i) test whether estimated energy expenditure or posture allocation from the devices was associated with weight change during and following a low-calorie diet (LCD) and (ii) compare free-living TEE and AEE predictions from the devices against DLW before weight change. Eighty-seven participants from 2 clinical trials wore 2 of the 3 devices simultaneously for 1 week of a 2-week DLW period. Participants then completed an 8-week LCD and were weighed at the start and end of the LCD and 6 and 12 months after the LCD. More time spent walking at baseline, measured by the IDEEA, significantly predicted greater weight loss during the 8-week LCD. Measures of posture allocation demonstrated medium effect sizes in their relationships with weight change. Bland-Altman analyses indicated that the Sensewear and the IDEEA accurately estimated TEE, and the IDEEA accurately measured AEE. The results suggest that the ability of energy expenditure and posture allocation to predict weight change is limited, and the accuracy of TEE and AEE measurements varies across activity monitoring devices, with multi-sensor monitors demonstrating stronger validity.
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Affiliation(s)
- John B Correa
- a Pennington Biomedical Research Center, Baton Rouge, LA 70808, USA
| | - John W Apolzan
- a Pennington Biomedical Research Center, Baton Rouge, LA 70808, USA
| | - Desti N Shepard
- a Pennington Biomedical Research Center, Baton Rouge, LA 70808, USA
| | | | - Jennifer C Rood
- a Pennington Biomedical Research Center, Baton Rouge, LA 70808, USA
| | - Corby K Martin
- a Pennington Biomedical Research Center, Baton Rouge, LA 70808, USA
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Hager ER, Treuth MS, Gormely C, Epps L, Snitker S, Black MM. Ankle Accelerometry for Assessing Physical Activity Among Adolescent Girls: Threshold Determination, Validity, Reliability, and Feasibility. RESEARCH QUARTERLY FOR EXERCISE AND SPORT 2015; 86:397-405. [PMID: 26288333 PMCID: PMC4775231 DOI: 10.1080/02701367.2015.1063574] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
PURPOSE Ankle accelerometry allows for 24-hr data collection and improves data volume/integrity versus hip accelerometry. Using Actical ankle accelerometry, the purpose of this study was to (a) develop sensitive/specific thresholds, (b) examine validity/reliability, (c) compare new thresholds with those of the manufacturer, and (d) examine feasibility in a community sample (low-income, urban adolescent girls). METHOD Two studies were conducted with 6th- through 7th-grade girls (aged 10-14 years old): First was a laboratory study (n = 24), in which 2 Actical accelerometers were placed on the ankle and worn while measuring energy expenditure (Cosmed K4b2, metabolic equivalents [METs]) during 10 prescribed activities. Analyses included device equivalence reliability (intraclass correlation coefficient [ICC], activity counts of 2 Acticals), criterion-related validity (correlation, activity counts and METs), and calculations of sensitivity, specificity, kappa, and receiver-operating characteristic curves for thresholds. The second was a free-living study (n = 459), in which an Actical was worn for more than 7 days on the ankle (full 24-hr days retained). Analyses included feasibility (frequencies, missing data) and paired t tests (new thresholds vs. those of the manufacturer). RESULTS In the laboratory study, the Actical demonstrated reliability (ICC = .92) and validity (r = .81). Thresholds demonstrated sensitivity (91%), specificity (84%), kappa = .73 (p = .043), area under curve range = .81-.97. In the free-living study, 99.6% of participants wore the accelerometer; 84.1% had complete/valid data (mean = 5.7 days). Primary reasons for missing/invalid data included: improper programming/documentation (5.2%), failure to return device (5.0%), and wear-time ≤ 2 days (2.8%). The moderate-to-vigorous physical activity threshold (> 3,200 counts/minute) yielded 37.2 min/day, 2 to 4.5 times lower than that of the manufacturer's software (effect size = 0.74-4.05). CONCLUSIONS Validity, reliability, and feasibility evidences support Actical ankle accelerometry to assess physical activity in community studies of adolescent girls. When comparing manufacturers' software versus new thresholds, a major difference was observed.
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Affiliation(s)
- Erin R. Hager
- University of Maryland School of Medicine, Department of Pediatrics
- University of Maryland School of Medicine, Department of Epidemiology and Public Health
| | - Margarita S. Treuth
- University of Maryland Eastern Shore, Departments of Physical Therapy and Exercise Science
| | - Candice Gormely
- University of Maryland School of Medicine, Department of Pediatrics
| | - LaShawna Epps
- University of Maryland School of Medicine, Department of Pediatrics
| | - Soren Snitker
- University of Maryland School of Medicine, Department of Pediatrics
- University of Maryland School of Medicine, Department of Medicine
| | - Maureen M. Black
- University of Maryland School of Medicine, Department of Pediatrics
- University of Maryland School of Medicine, Department of Epidemiology and Public Health
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Ahn GE, Chmiel JS, Dunlop DD, Helenowski IB, Semanik PA, Song J, Ainsworth B, Chang RW, Ramsey-Goldman R. Self-reported and objectively measured physical activity in adults with systemic lupus erythematosus. Arthritis Care Res (Hoboken) 2015; 67:701-7. [PMID: 25251755 DOI: 10.1002/acr.22480] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2014] [Revised: 08/14/2014] [Accepted: 09/16/2014] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Most estimates of physical activity (PA) patterns in systemic lupus erythematosus (SLE) are based on subjective self-report measures prone to error. The aims of this study were to obtain objective measurements of PA using an accelerometer and estimates of energy expenditure based on the self-reported International Physical Activity Questionnaire (IPAQ), and to describe their relationship. METHODS The Activity in Lupus To Energize and Renew study, a cross-sectional study of PA, included 129 persons with SLE. Accelerometer measures >7 days included total daily activity counts and minutes of moderate-vigorous PA (MVPA). Each person completed the IPAQ via telephone interview. Spearman's correlations (r) and 95% confidence intervals (95% CIs) assessed associations between accelerometer and IPAQ. RESULTS Daily PA means and SDs from accelerometer measures were 502,910 ± 118,755 for total daily activity counts and 40 ± 30 minutes for MVPA. The median (interquartile range) MET minutes/day for IPAQ intensities were: 400 (159-693) for total; 83 (26-184) for walking; and 231 (77-514) for MVPA, and domains were: 0 (0-73) for work; 28 (0-85) for active transportation; 77 (26-231) for domestic and garden; and 57 (0-213) for leisure. Associations between accelerometer measures and IPAQ were 1) total daily count versus IPAQ total: r = 0.21 (95% CI 0.03, 0.37) and 2) MVPA versus IPAQ moderate-vigorous: r = 0.16 (95% CI - 230.02, 0.33). CONCLUSION Accelerometer measures and IPAQ energy expenditure estimates were moderately correlated. IPAQ provided descriptive PA data, whereas accelerometers captured all daily activities, which can help assess guideline attainment. The choice of IPAQ versus accelerometer measure should include considering the purpose for which PA is measured.
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Jones SA, Evenson KR, Johnston LF, Trost SG, Samuel-Hodge C, Jewell DA, Kraschnewski JL, Keyserling TC. Psychometric properties of the modified RESIDE physical activity questionnaire among low-income overweight women. J Sci Med Sport 2015; 18:37-42. [PMID: 24462117 PMCID: PMC4184999 DOI: 10.1016/j.jsams.2013.12.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2013] [Revised: 12/04/2013] [Accepted: 12/13/2013] [Indexed: 10/25/2022]
Abstract
OBJECTIVES This study explored the criterion-related validity and test-retest reliability of the modified RESIDential Environment physical activity questionnaire and whether the instrument's validity varied by body mass index, education, race/ethnicity, or employment status. DESIGN Validation study using baseline data collected for randomized trial of a weight loss intervention. METHODS Participants recruited from health departments wore an ActiGraph accelerometer and self-reported non-occupational walking, moderate and vigorous physical activity on the modified RESIDential Environment questionnaire. We assessed validity (n=152) using Spearman correlation coefficients, and reliability (n=57) using intraclass correlation coefficients. RESULTS When compared to steps, moderate physical activity, and bouts of moderate/vigorous physical activity measured by accelerometer, these questionnaire measures showed fair evidence for validity: recreational walking (Spearman correlation coefficients 0.23-0.36), total walking (Spearman correlation coefficients 0.24-0.37), and total moderate physical activity (Spearman correlation coefficients 0.18-0.36). Correlations for self-reported walking and moderate physical activity were higher among unemployed participants and women with lower body mass indices. Generally no other variability in the validity of the instrument was found. Evidence for reliability of RESIDential Environment measures of recreational walking, total walking, and total moderate physical activity was substantial (intraclass correlation coefficients 0.56-0.68). CONCLUSIONS Evidence for questionnaire validity and reliability varied by activity domain and was strongest for walking measures. The questionnaire may capture physical activity less accurately among women with higher body mass indices and employed participants. Capturing occupational activity, specifically walking at work, may improve questionnaire validity.
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Affiliation(s)
- Sydney A Jones
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, United States
| | - Kelly R Evenson
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, United States.
| | - Larry F Johnston
- UNC Center for Health Promotion and Disease Prevention, The University of North Carolina at Chapel Hill, United States
| | - Stewart G Trost
- School of Human Movement Studies, The University of Queensland, Australia
| | - Carmen Samuel-Hodge
- Department of Nutrition, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, United States
| | - David A Jewell
- School of Arts and Sciences, Johnson & Wales University Charlotte Campus, United States
| | - Jennifer L Kraschnewski
- Department of Medicine and Public Health Sciences, Penn State College of Medicine, United States
| | - Thomas C Keyserling
- Department of Medicine, School of Medicine, The University of North Carolina at Chapel Hill, United States
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Physical activity monitoring in extremely obese adolescents from the Teen-LABORATORIES study. J Phys Act Health 2014; 12:132-8. [PMID: 25205688 DOI: 10.1123/jpah.2013-0006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
BACKGROUND The accuracy of physical activity (PA) monitors to discriminate between PA, sedentary behavior, and nonwear in extremely obese (EO) adolescents is unknown. METHODS Twenty-five subjects (9 male/16 female; age = 16.5 ± 2.0 y; BMI = 51 ± 8 kg/m2) wore 3 activity monitors (StepWatch [SAM], Actical [AC], Actiheart [AH]) during a 400-m walk test (400MWT), 2 standardized PA bouts of varying duration, and 1 sedentary bout. RESULTS For the 400MWT, percent error between observed and monitor-recorded steps was 5.5 ± 7.1% and 82.1 ± 38.6% for the SAM and AC steps, respectively (observed vs. SAM steps: -17.2 ± 22.2 steps; observed vs. AC steps: -264.5 ± 124.8 steps). All activity monitors were able to differentiate between PA and sedentary bouts, but only SAM steps and AH heart rate were significantly different between sedentary behavior and nonwear (P < .001). For all monitors, sedentary behavior was characterized by bouts of zero steps/counts punctuated by intermittent activity steps/counts; nonwear was represented almost exclusively by zero steps/counts. CONCLUSION Of all monitors tested, the SAM was most accurate in terms of counting steps and differentiating levels of PA and thus, most appropriate for EO adolescents. The ability to accurately characterize PA intensity in EO adolescents critically depends on activity monitor selection.
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Aadland E, Steene-Johannessen J. The use of individual cut points from treadmill walking to assess free-living moderate to vigorous physical activity in obese subjects by accelerometry: is it useful? BMC Med Res Methodol 2012; 12:172. [PMID: 23152980 PMCID: PMC3533971 DOI: 10.1186/1471-2288-12-172] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2012] [Accepted: 11/06/2012] [Indexed: 11/10/2022] Open
Abstract
Background Variation in counts between subjects at a given speed or work rate are the most important source of error in physical activity (PA) measurements with accelerometers. The aim of this study was to explore how the use of individual accelerometer cut points (ICPs) affected the analysis of PA field data. Methods We performed a treadmill calibration protocol to determine cut points for moderate to vigorous PA (MVPA) (≥3 metabolic equivalents) and assessed free-living PA in 44 severely obese subjects using the Actigraph GT1M accelerometer. We obtained cut points in 42 subjects (11 men, mean (standard deviation) of body mass index (BMI) 39.8 (5.7), age 43.2 (9.2) years), of whom 35 had valid measurement of free-living PA (minutes of MVPA/day). Linear regression was used to analyze associations with the ICPs and time in MVPA/day. MVPA/day was also compared with values derived using a group cut point (GCP). Results Resting oxygen consumption (partial r = 0.74, p < .001), work economy (partial r = −0.76, p < .001) and BMI (partial r = 0.52, p = .001) explained 68.4% of the variation in the ICPs (F = 26.7, p < .001). The ICPs explained 79.1% of the variation in the minutes spent in MVPA/day. Moderate to vigorous PA/day derived from the ICPs vs. the GCP varied substantially (R2 = 14%, p = .023, coefficient of variation = 45.1%). Conclusions The results indicate that the use of ICPs had a strong influence on the PA level. Two thirds of the variation in the ICPs could be explained, however, a certain degree of measurement error will be present. Thus, we are not able to conclude with respect to the most appropriate procedure for analyzing time in MVPA.
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Abstract
Measurement of physical activity is important, given the vital role of this behavior in physical and mental health. Over the past quarter of a century, the use of small, non-invasive, wearable monitors to assess physical activity has become commonplace. This review is divided into three sections. In the first section, a brief history of physical activity monitoring is provided, along with a discussion of the strengths and weaknesses of different devices. In the second section, recent applications of physical activity monitoring in physical activity and public health research are discussed. Wearable monitors are being used to conduct surveillance, and to determine the extent and distribution of physical activity and sedentary behaviors in populations around the world. They have been used to help clarify the dose-response relation between physical activity and health. Wearable monitors that provide feedback to users have also been used in longitudinal interventions to motivate research participants and to assess their compliance with program goals. In the third section, future directions for research in physical activity monitoring are discussed. It is likely that new developments in wearable monitors will lead to greater accuracy and improved ease-of-use.
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Straker L, Campbell A. Translation equations to compare ActiGraph GT3X and Actical accelerometers activity counts. BMC Med Res Methodol 2012; 12:54. [PMID: 22520344 PMCID: PMC3439688 DOI: 10.1186/1471-2288-12-54] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2011] [Accepted: 04/20/2012] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND This study aimed to develop a translation equation to enable comparison between Actical and ActiGraph GT3X accelerometer counts recorded minute by minute. METHODS Five males and five females of variable height, weight, body mass index and age participated in this investigation. Participants simultaneously wore an Actical and an ActiGraph accelerometer for two days. Conversion algorithms and R(2) were calculated day by day for each subject between the omnidirectional Actical and three different ActiGraph (three-dimensional) outputs: 1) vertical direction, 2) combined vector, and 3) a custom vector. Three conversion algorithms suitable for minute/minute conversions were then calculated from the full data set. RESULTS The vertical ActiGraph activity counts demonstrated the closest relationship with the Actical, with consistent moderate to strong conversions using the algorithm: y = 0.905x, in the day by day data (R(2) range: 0.514 to 0.989 and average: 0.822) and full data set (R(2) = 0.865). CONCLUSIONS The Actical is most sensitive to accelerations in the vertical direction, and does not closely correlate with three-dimensional ActiGraph output. Minute by minute conversions between the Actical and ActiGraph vertical component can be confidently performed between data sets and might allow further synthesis of information between studies.
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Affiliation(s)
- Leon Straker
- School of Physiotherapy and Curtin Health Innovation Research Institute, Curtin University, Perth, Australia.
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Aadland E, Anderssen SA. Treadmill Calibration of the Actigraph GT1M in Young-to-Middle-Aged Obese-to-Severely Obese Subjects. J Obes 2012. [PMID: 23193449 PMCID: PMC3502012 DOI: 10.1155/2012/318176] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
To understand the impact of physical activity (PA) on health, valid accelerometer count cut points must be applied to measure PA. Because cut points may be population specific, we aimed to establish accelerometer cut points for moderate PA (MPA) and vigorous PA (VPA) (defined as ≥3 and ≥6 metabolic equivalents, resp.) in young-to-middle-aged obese-to-severely obese subjects. Data from 42 subjects (11 men; body mass index 39.8 ± 5.7; age 43.2 ± 9.2 years) who performed a treadmill calibration using the Actigraph GT1M, were analyzed using ordinary linear regression (OLR), linear mixed model regression (MIX), and receiver operating characteristics curves (ROC 1; ROC 2). Cut points obtained from the models were quite different (612 to 1646 counts/min for MPA; 3061 to 7220 counts/min for VPA). We argue that the MIX approach, which resulted in cut points of 612 and 4980 counts/min for MPA and VPA, respectively, is the most appropriate method to establish accelerometer cut points in this setting. We conclude that accelerometer cut points are lower in young-to-middle-aged obese-to-severely obese subjects compared to young normal-weight subjects and that care should be taken when analyzing PA level in groups that vary in age and degree of obesity.
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Affiliation(s)
- Eivind Aadland
- Faculty of Health Studies, Sogn og Fjordane University College, P.O. Box 523, 6803 Førde, Norway
- *Eivind Aadland:
| | - Sigmund Alfred Anderssen
- Department of Sports Medicine, Norwegian School of Sport Sciences, P.O. Box 4014, Ullevaal Stadion, 0806 Oslo, Norway
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Cleland I, Nugent CD, Finlay DD, Burns W, Bougourd J, Stevens K, Armitage R. Effects of BMI and abdominal volume on the accuracy of step count obtained from a tri-axial accelerometer. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2011; 2011:3656-3659. [PMID: 22255132 DOI: 10.1109/iembs.2011.6090616] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Accelerometers are widely accepted as practical wearable devices capable of measuring and assessing physical activity. These devices may, however, be subject to errors which could impact on their ability to acquire an accurate step count. A limited number of studies have examined the effects of body mass index (BMI) on the accuracy of accelerometers functioning as step counters. It has been suggested that BMI may not be the best indicator of adiposity. The aim of the present study was to assess the effects of BMI and abdominal volume on the accuracy of a step count obtained from a tri-axial accelerometer. Accelerometers were placed directly onto the skin at the chest, waist and lower back of 12 participants. Participants then walked on a motorized treadmill at 0.89 m/s and 1.34 m/s. Analysis of the results indicated that BMI and abdominal volume did not affect the accuracy of the step count obtained from accelerometers under any conditions. Walking speed, however, had a significant effect with step count accuracy decreasing at the slower speed.
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Affiliation(s)
- Ian Cleland
- Computer Science Research Institute and School of Computing and Mathematics University of Ulster, Newtownabbey, Co Antrim, Northern Ireland BT37 0QB.
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