Scott A. Conger

Classification Accuracy of the Wrist-Worn Gravity Estimator of Normal Everyday Activity Accelerometer

PURPOSE The purpose of this study was to determine whether the published left-wrist cut points for the triaxial Gravity Estimator of Normal Everyday Activity (GENEA) accelerometer are accurate for predicting intensity categories during structured activity bouts. METHODS A convenience sample of 130 adults wore a GENEA accelerometer on their left wrist while performing 14 different lifestyle activities. During each activity, oxygen consumption was continuously measured using the Oxycon mobile. Statistical analysis used Spearmans rank correlations to determine the relationship between measured and estimated intensity classifications. Cross tabulations were constructed to show the under- or overestimation of misclassified intensities. One-way χ2 tests were used to determine whether the intensity classification accuracy for each activity differed from 80%. RESULTS For all activities, the GENEA accelerometer-based physical activity monitor explained 41.1% of the variance in energy expenditure. The intensity classification accuracy was 69.8% for sedentary activities, 44.9% for light activities, 46.2% for moderate activities, and 77.7% for vigorous activities. The GENEA correctly classified intensity for 52.9% of observations when all activities were examined; this increased to 61.5% with stationary cycling removed. CONCLUSIONS A wrist-worn triaxial accelerometer has modest-intensity classification accuracy across a broad range of activities when using the cut points of Esliger et al. Although the sensitivity and the specificity are less than those reported by Esliger et al., they are generally in the same range as those reported for waist-worn, uniaxial accelerometer cut points.

Trends in Physical Activity and Sedentary Behaviors of United States Youth

BACKGROUND Increases in childhood and adolescent obesity are a growing concern in the United States (U.S.), and in most countries throughout the world. Declines in physical activity are often postulated to have contributed to the rise in obesity rates during the past 40 years. METHODS We searched for studies of trends in physical activity and sedentary behaviors of U.S. youth, using nontraditional data sources. Literature searches were conducted for active commuting, physical education, high-school sports, and outdoor play. In addition, trends in sedentary behaviors were examined. RESULTS Data from the Youth Risk Behavior Surveillance System (YRBSS) and other national surveys, as well as longitudinal studies in the transportation, education, electronic media, and recreation sectors showed evidence of changes in several indicators. Active commuting, high school physical education, and outdoor play (in 3- to 12-year-olds) declined over time, while sports participation in high school girls increased from 1971 to 2012. In addition, electronic entertainment and computer use increased during the first decade of the 21st century. CONCLUSIONS Technological and societal changes have impacted the types of physical activities performed by U.S. youth. These data are helpful in understanding the factors associated with the rise in obesity, and in proposing potential solutions.

Detection of Lying Down, Sitting, Standing, and Stepping Using Two ActivPAL Monitors

UNLABELLED The activPAL is an accelerometer-based monitor worn on the thigh that classifies daily activities into three categories (sitting/lying down, standing, and stepping). The monitor discriminates between sitting/lying and the upright position by detecting the inclination of the thigh. It detects stepping from the acceleration versus time wave form. However, a current limitation of the activPAL is that it does not discriminate between sitting and lying down. PURPOSE This study aimed to determine whether placing a second activPAL monitor on the torso would allow the detection of seated versus lying postures. METHODS Fifteen healthy adults (18-55 yr of age) wore an activPAL on the right thigh and another activPAL over the right rib cage. Both monitors were synchronized and initialized to record data in 15-s epochs. Participants performed a semistructured routine of activities for 3 min each. Activities included lying down (while supine, prone, and on the side), sitting, standing, sweeping, treadmill walking at 3 mph, and treadmill running at 6 mph. The spatial orientation of the thigh and chest monitors was used to determine body posture, and the activPAL on the thigh was used to detect ambulation. RESULTS The use of two activPAL devices enabled four behaviors to be accurately classified. The percentages of observations that were classified accurately were as follows: lying down (100%), sitting (100%), standing/light activity in the upright position (90.8%), and stepping (100%). CONCLUSIONS The current method allows researchers to obtain more detailed information on postural allocation compared with that in the use of a single activPAL on the thigh.

Detecting indoor and outdoor environments using the ActiGraph GT3X+ light sensor in children.

INTRODUCTION Experts recommend children spend more time playing outdoors. The ambient light sensor of the ActiGraph GT3X+ provides lux measurements. A lux is the International Systems unit of illumination, equivalent to 1 lm·m. Few studies have established a lux threshold for determining whether a child is indoors or outdoors. PURPOSE This study aimed 1) to assess the reliability of the ActiGraph GT3X+ ambient light sensor, 2) to identify a lux threshold to accurately discriminate between indoor and outdoor activities in children, and 3) to test the accuracy of the lux threshold in a free-living environment. METHODS In part 1, a series of reliability tests were performed using 20 ActiGraph GT3X+ monitors under different environmental conditions. Cronbachs alpha was used to determine interinstrument reliability. In part 2, 18 children performed 11 different activities (five indoors and six outdoors) for 6 min each. The optimal threshold for detecting indoor/outdoor activity was determined using a receiver operator characteristic curve analysis. In part 3, 18 children at a preschool wore the monitor during a school day. Percent accuracy was determined for all conditions. RESULTS In part 1, the devices had Cronbachs alpha values of 0.992 and 1.000 for indoor and outdoor conditions, respectively, indicating high interinstrument reliability. In part 2, the optimal lux threshold was determined to be 240 lux (sensitivity = 0.92, specificity = 0.88, area under the curve = 0.96, 95% CI = 0.951-0.970). In part 3, results of the school-day validation demonstrated the monitor was 97.0% accurate for overall detection of indoor and outdoor conditions (outdoor = 88.9%, indoor = 99.1%). CONCLUSIONS The results demonstrate that an ActiGraph GT3X+ lux threshold of 240 can accurately assess indoor and outdoor conditions of preschool children in a free-living environment.

Validity of Physical Activity Monitors for Estimating Energy Expenditure During Wheelchair Propulsion

BACKGROUND It is unknown if activity monitors can detect the increased energy expenditure (EE) of wheelchair propulsion at different speeds or on different surfaces. METHODS Individuals who used manual wheelchairs (n = 14) performed 5 wheeling activities: on a level surface at 3 speeds, on a rubberized track at 1 fixed speed and on a sidewalk course at a self-selected speed. EE was measured using a portable indirect calorimetry system and estimated by an Actical (AC) worn on the wrist and a SenseWear (SW) activity monitor worn on the upper arm. Repeated- measures ANOVA was used to compare measured EE to the estimates from the standard AC prediction equation and SW using 2 different equations. RESULTS Repeated-measures ANOVA demonstrated a significant main effect between measured EE and estimated EE. There were no differences between the criterion method and the AC across the 5 activities. The SW overestimated EE when wheeling at 3 speeds on a level surface, and during sidewalk wheeling. The wheelchair-specific SW equation improved the EE prediction during low intensity activities, but error progressively increased during higher intensity activities. CONCLUSIONS During manual wheelchair propulsion, the wrist-mounted AC provided valid estimates of EE, whereas the SW tended to overestimate EE.

Cross-validation of Waist-Worn GENEA Accelerometer Cut-Points.

PURPOSE The purpose of this study was to determine the classification accuracy of the waist gravity estimator of normal everyday activity (GENEA) cut-points developed by Esliger et al. for predicting intensity categories across a range of lifestyle activities. METHODS Each participant performed one of two routines, consisting of seven lifestyle activities (home/office, ambulatory, and sport). The GENEA was worn on the right waist, and oxygen uptake was continuously measured using the Oxycon mobile. A one-way chi-squared test was used to determine the classification accuracy of the GENEA cut-points. Cross-tabulation tables provided information on under- and overestimations, and sensitivity and specificity analyses of the waist cut-points were also performed. RESULTS Spearman rank order correlation for the GENEA gravity-subtracted signal vector magnitude and Oxycon mobile MET values was 0.73. For all activities combined, the GENEA accurately predicted intensity classification 55.3% of the time, and it increased to 58.3% when stationary cycling was removed from the analysis. The sensitivity of the cut-points for the four intensity categories ranged from 0.244 to 0.958, and the specificity ranged from 0.576 to 0.943. CONCLUSION In this cross-validation study, the proposed GENEA cut-points had a low overall accuracy rate for classifying intensity (55.3%) when engaging in 14 different lifestyle activities.

Objective Assessment of Strength Training Exercises Using a Wrist-Worn Accelerometer

UNLABELLED The 2008 Physical Activity Guidelines for Americans recommend that all adults perform muscle-strengthening exercises to work all of the major muscle groups of the body on at least 2 d·wk, in addition to aerobic activity. Studies using objective methods of monitoring physical activity have focused primarily on the assessment of aerobic activity. To date, a method for assessing resistance training (RT) exercises has not been developed using a wrist-worn activity monitor. PURPOSE The purpose of this study was to examine the use of a wrist-worn triaxial accelerometer-based activity monitor for classifying upper- and lower-body dumbbell RT exercises. METHODS Sixty participants performed 10 repetitions each of 12 different upper- and lower-body dynamic dumbbell exercises. Algorithms for classifying the exercises were developed using two different methods: support vector machine and cosine similarity. Confusion matrices were developed for each method, and intermethod reliabilities were assessed using Cohens kappa. A repeated-measures ANOVA was used to compare the predicted repetitions, identified from the largest acceleration peaks, with the actual repetitions. RESULTS The results indicated that support vector machine and cosine similarity accurately classified the 12 different RT exercises 78% and 85% of the time, respectively. Both methods struggled to correctly differentiate bench press versus shoulder press and squat versus walking lunges. Repetition estimates were not significantly different for 8 of the 12 exercises. For the four exercises that were significantly different, the differences amount to less than 10%. CONCLUSION This study demonstrated that RT exercises can be accurately classified using a single activity monitor worn on the wrist.

Predicting energy expenditure through hand rim propulsion power output in individuals who use wheelchairs

Aim To examine the relationship between hand rim propulsion power and energy expenditure (EE) during wheelchair wheeling and to investigate whether adding other variables to the model could improve on the prediction of EE. Methods Individuals who use manual wheelchairs (n=14) performed five different wheeling activities in a wheelchair with a PowerTap power meter hub built into the right rear wheel. Activities included wheeling on a smooth, level surface at three different speeds (4.5, 5.5 and 6.5 km/h), wheeling on a rubberised track at one speed (5.5 km/h) and wheeling on a sidewalk course that included uphill and downhill segments at a self-selected speed. EE was measured using a portable indirect calorimetry system. Stepwise linear regression was performed to predict EE from power output variables. A repeated-measures analysis of variance was used to compare the measured EE to the estimates from the power models. Bland-Altman plots were used to assess the agreement between the criterion values and the predicted values. Results EE and power were significantly correlated (r=0.694, p<0.001). Regression analysis yielded three significant prediction models utilising measured power; measured power and speed; and measured power, speed and heart rate. No significant differences were found between measured EE and any of the prediction models. Conclusion EE can be accurately and precisely estimated based on hand rim propulsion power. These results indicate that power could be used as a method to assess EE in individuals who use wheelchairs.

Energy Expenditure for 70 Activities in Children and Adolescents

BACKGROUND Thirty-five percent of the activities assigned MET values in the Compendium of Energy Expenditures for Youth were obtained from direct measurement of energy expenditure (EE). The aim of this study was to provide directly measured EE for several different activities in youth. METHODS Resting metabolic rate (RMR) of 178 youths (80 females, 98 males) was first measured. Participants then performed structured activity bouts while wearing a portable metabolic system to directly measure EE. Steady-state oxygen consumption data were used to compute activity METstandard (activity VO2/3.5) and METmeasured (activity VO2/measured RMR) for the different activities. RESULTS Rates of EE were measured for 70 different activities and ranged from 1.9 to 12.0 METstandard and 1.5 to 10.0 METmeasured. CONCLUSION This study provides directly measured energy cost values for 70 activities in children and adolescents. It contributes empirical data to support the expansion of the Compendium of Energy Expenditures for Youth.

Postprandial Walking Reduces Glucose Levels in Women with Gestational Diabetes Mellitus

The purpose of this study was to investigate blood glucose changes, as measured by a continuous glucose monitoring system, that occur in women with gestational diabetes mellitus (GDM) following an acute bout of moderate-intensity walking after consuming a high-carbohydrate/low-fat meal. This study found that moderate-intensity walking induced greater postprandial glucose control compared with sedentary activity and it appears that moderate-intensity activity may be used to reduce postprandial glucose levels in women with GDM.