A calibration protocol for population-specific accelerometer cut-points in children

PurposeTo test a field-based protocol using intermittent activities representative of children\u27s physical activity behaviours, to generate behaviourally valid, population-specific accelerometer cut-points for sedentary behaviour, moderate, and vigorous physical activity.MethodsTwenty-eight children (46% boys) aged 10&ndash;11 years wore a hip-mounted uniaxial GT1M ActiGraph and engaged in 6 activities representative of children\u27s play. A validated direct observation protocol was used as the criterion measure of physical activity. Receiver Operating Characteristics (ROC) curve analyses were conducted with four semi-structured activities to determine the accelerometer cut-points. To examine classification differences, cut-points were cross-validated with free-play and DVD viewing activities.ResultsCut-points of &le;372, &gt;2160 and &gt;4806 counts&bull;min&minus;1 representing sedentary, moderate and vigorous intensity thresholds, respectively, provided the optimal balance between the related needs for sensitivity (accurately detecting activity) and specificity (limiting misclassification of the activity). Cross-validation data demonstrated that these values yielded the best overall kappa scores (0.97; 0.71; 0.62), and a high classification agreement (98.6%; 89.0%; 87.2%), respectively. Specificity values of 96&ndash;97% showed that the developed cut-points accurately detected physical activity, and sensitivity values (89&ndash;99%) indicated that minutes of activity were seldom incorrectly classified as inactivity.ConclusionThe development of an inexpensive and replicable field-based protocol to generate behaviourally valid and population-specific accelerometer cut-points may improve the classification of physical activity levels in children, which could enhance subsequent intervention and observational studies.<br /

Comparison of two different physical activity monitors

<p>Abstract</p> <p>Background</p> <p>Understanding the relationships between physical activity (PA) and disease has become a major area of research interest. Activity monitors, devices that quantify free-living PA for prolonged periods of time (days or weeks), are increasingly being used to estimate PA. A range of different activity monitors brands are available for investigators to use, but little is known about how they respond to different levels of PA in the field, nor if data conversion between brands is possible.</p> <p>Methods</p> <p>56 women and men were fitted with two different activity monitors, the Actigraph™ (Actigraph LLC; AGR) and the Actical™ (Mini-Mitter Co.; MM) for 15 days. Both activity monitors were fixed to an elasticized belt worn over the hip, with the anterior and posterior position of the activity monitors randomized. Differences between activity monitors and the validity of brand inter-conversion were measured by <it>t</it>-tests, Pearson correlations, Bland-Altman plots, and coefficients of variation (CV).</p> <p>Results</p> <p>The AGR detected a significantly greater amount of daily PA (216.2 ± 106.2 vs. 188.0 ± 101.1 counts/min, P < 0.0001). The average difference between activity monitors expressed as a CV were 3.1 and 15.5% for log-transformed and raw data, respectively. When a conversion equation was applied to convert datasets from one brand to another, the differences were no longer significant, with CV's of 2.2 and 11.7%, log-transformed and raw data, respectively.</p> <p>Conclusion</p> <p>Although activity monitors predict PA on the same scale (counts/min), the results between these two brands are not directly comparable. However, the data are comparable if a conversion equation is applied, with better results for log-transformed data.</p

A Machine Learning Approach to Measure and Monitor Physical Activity in Children to Help Fight Overweight and Obesity

Physical Activity is important for maintaining healthy lifestyles. Recommendations for physical activity levels are issued by most governments as part of public health measures. As such, reliable measurement of physical activity for regulatory purposes is vital. This has lead research to explore standards for achieving this using wearable technology and artificial neural networks that produce classifications for specific physical activity events. Applied from a very early age, the ubiquitous capture of physical activity data using mobile and wearable technology may help us to understand how we can combat childhood obesity and the impact that this has in later life. A supervised machine learning approach is adopted in this paper that utilizes data obtained from accelerometer sensors worn by children in free-living environments. The paper presents a set of activities and features suitable for measuring physical activity and evaluates the use of a Multilayer Perceptron neural network to classify physical activities by activity type. A rigorous reproducible data science methodology is presented for subsequent use in physical activity research. Our results show that it was possible to obtain an overall accuracy of 96 % with 95 % for sensitivity, 99 % for specificity and a kappa value of 94 % when three and four feature combinations were used

Validity of physical activity monitors for assessing lower intensity activity in adults

Background: Accelerometers can provide accurate estimates of moderate-to-vigorous physical activity (MVPA). However, one of the limitations of these instruments is the inability to capture light activity within an acceptable range of error. The purpose of the present study was to determine the validity of different activity monitors for estimating energy expenditure (EE) of light intensity, semi-structured activities. Methods: Forty healthy participants wore a SenseWear Pro3 Armband (SWA, v.6.1), the SenseWear Mini, the Actiheart, ActiGraph, and ActivPAL monitors, while being monitored with a portable indirect calorimetry (IC). Participants engaged in a variety of low intensity activities but no formalized scripts or protocols were used during these periods. Results: The Mini and SWA overestimated total EE on average by 1.0% and 4.0%, respectively, while the AH, the GT3X, and the AP underestimated total EE on average by 7.8%, 25.5%, and 22.2%, respectively. The pattern-recognition monitors yielded non-significant differences in EE estimates during the semi-structured period (p = 0.66, p = 0.27, and p = 0.21 for the Mini, SWA, and AH, respectively). Conclusions: The SenseWear Mini provided more accurate estimates of EE during light to moderate intensity semi-structured activities compared to other activity monitors. This monitor should be considered when there is interest in tracking low intensity activities in groups of individuals.This research was funded by a grant from Bodymedia Inc. awarded to Dr. Greg Welk

Walks4work: Rationale and study design to investigate walking at lunchtime in the workplace setting

Background: Following recruitment of a private sector company, an 8week lunchtime walking intervention was implemented to examine the effect of the intervention on modifiable cardiovascular disease risk factors, and further to see if walking environment had any further effect on the cardiovascular disease risk factors. Methods. For phase 1 of the study participants were divided into three groups, two lunchtime walking intervention groups to walk around either an urban or natural environment twice a week during their lunch break over an 8week period. The third group was a waiting-list control who would be invited to join the walking groups after phase 1. In phase 2 all participants were encouraged to walk during their lunch break on self-selecting routes. Health checks were completed at baseline, end of phase 1 and end of phase 2 in order to measure the impact of the intervention on cardiovascular disease risk. The primary outcome variables of heart rate and heart rate variability were measured to assess autonomic function associated with cardiovascular disease. Secondary outcome variables (Body mass index, blood pressure, fitness, autonomic response to a stressor) related to cardiovascular disease were also measured. The efficacy of the intervention in increasing physical activity was objectively monitored throughout the 8-weeks using an accelerometer device. Discussion. The results of this study will help in developing interventions with low researcher input with high participant output that may be implemented in the workplace. If effective, this study will highlight the contribution that natural environments can make in the reduction of modifiable cardiovascular disease risk factors within the workplace. © 2012 Brown et al.; licensee BioMed Central Ltd

Calibration of GENEActiv accelerometer wrist cut-points for the assessment of physical activity intensity of pre-school aged children

This study sought to validate cut-points for use of wrist worn GENEActiv accelerometer data, to analyse preschool children’s (4 to 5 year olds) physical activity (PA) levels via calibration with oxygen consumption values (VO2). This was a laboratory based calibration study. Twenty-one preschool children, aged 4.7 ± 0.5 years old, completed six activities (ranging from lying supine to running) whilst wearing the GENEActiv accelerometers at two locations (left and right wrist), these being the participants’ non-dominant and dominant wrist, and a Cortex face mask for gas analysis. VO2 data was used for the assessment of criterion validity. Location specific activity intensity cut points were established via Receiver Operator Characteristic curve (ROC) analysis. The GENEActiv accelerometers, irrespective of their location, accurately discriminated between all PA intensities (sedentary, light, and moderate and above), with the dominant wrist monitor providing a slightly more precise discrimination at light PA and the non-dominant at the sedentary behaviour and moderate and above intensity levels (Area Under the Curve (AUC) for non-dominant = 0.749-0.993, compared to AUC dominant = 0.760-0.988). Conclusion: This study establishes wrist-worn physical activity cut points for the GENEActiv accelerometer in pre-schoolers.N/

The contribution of office work to sedentary behaviour associated risk

Background: Sedentary time has been found to be independently associated with poor health and mortality. Further, a greater proportion of the workforce is now employed in low activity occupations such as office work. To date, there is no research that specifically examines the contribution of sedentary work to overall sedentary exposure and thus risk. The purpose of the study was to determine the total exposure and exposure pattern for sedentary time, light activity and moderate/vigorous physical activity (MVPA) of office workers during work and non-work time.Methods: 50 office workers from Perth, Australia wore an Actical (Phillips, Respironics) accelerometer during waking hours for 7 days (in 2008–2009). Participants recorded wear time, waking hours, work hours and daily activities in an activity diary. Time in activity levels (as percentage of wear time) during work and non-work time were analysed using paired t-tests and Pearson’s correlations.Results: Sedentary time accounted for 81.8% of work hours (light activity 15.3% and MVPA 2.9%), which was significantly greater than sedentary time during non-work time (68.9% p 30 minutes) and significantly less brief duration (0–10 minutes) light intensity activity during work hours compared to non-work time (p < 0.001). Further, office workers had fewer breaks in sedentary time during work hours compared to non-work time (p < 0.001).Conclusions: Office work is characterised by sustained sedentary time and contributes significantly to overall sedentary exposure of office workers

Step detection and activity recognition accuracy of seven physical activity monitors

The aim of this study was to compare the seven following commercially available activity monitors in terms of step count detection accuracy: Movemonitor (Mc Roberts), Up (Jawbone), One (Fitbit), ActivPAL (PAL Technologies Ltd.), Nike+ Fuelband (Nike Inc.), Tractivity (Kineteks Corp.) and Sensewear Armband Mini (Bodymedia). Sixteen healthy adults consented to take part in the study. The experimental protocol included walking along an indoor straight walkway, descending and ascending 24 steps, free outdoor walking and free indoor walking. These tasks were repeated at three self-selected walking speeds. Angular velocity signals collected at both shanks using two wireless inertial measurement units (OPAL, ADPM Inc) were used as a reference for the step count, computed using previously validated algorithms. Step detection accuracy was assessed using the mean absolute percentage error computed for each sensor. The Movemonitor and the ActivPAL were also tested within a nine-minute activity recognition protocol, during which the participants performed a set of complex tasks. Posture classifications were obtained from the two monitors and expressed as a percentage of the total task duration. The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count. The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking. The activity recognition protocol showed that the Movemonitor performed best in the walking recognition, but had difficulty in discriminating between standing and sitting. Results of this study can be used to inform choice of a monitor for specific applications