Randal C. Foster

Energy expenditure of sedentary screen time compared with active screen time for children.

OBJECTIVE. We examined the effect of activity-enhancing screen devices on childrens energy expenditure compared with performing the same activities while seated. Our hypothesis was that energy expenditure would be significantly greater when children played activity-promoting video games, compared with sedentary video games. METHODS. Energy expenditure was measured for 25 children aged 8 to 12 years, 15 of whom were lean, while they were watching television seated, playing a traditional video game seated, watching television while walking on a treadmill at 1.5 miles per hour, and playing activity-promoting video games. RESULTS. Watching television and playing video games while seated increased energy expenditure by 20 ± 13% and 22 ± 12% above resting values, respectively. When subjects were walking on the treadmill and watching television, energy expenditure increased by 138 ± 40% over resting values. For the activity-promoting video games, energy expenditure increased by 108 ± 40% with the EyeToy (Sony Computer Entertainment) and by 172 ± 68% with Dance Dance Revolution Ultramix 2 (Konami Digital Entertainment). CONCLUSIONS. Energy expenditure more than doubles when sedentary screen time is converted to active screen time. Such interventions might be considered for obesity prevention and treatment.

Activity-promoting video games and increased energy expenditure.

OBJECTIVES To test the hypothesis that both children and adults would expend more calories and move more while playing activity-promoting video games compared with sedentary video games. STUDY DESIGN In this single-group study, 22 healthy children (12 +/- 2 years; 11 male, 11 female) and 20 adults (34 +/- 11 years; 10 male, 10 female) were recruited. Energy expenditure and physical activity were measured while participants were resting, standing, watching television seated, sitting and playing a traditional sedentary video game, and while playing an activity-promoting video game (Nintendo Wii Boxing). Physical activity was measured with accelerometers, and energy expenditure was measured with an indirect calorimeter. RESULTS Energy expenditure was significantly greater than all other activities when children or adults played Nintendo Wii (mean increase over resting, 189 +/- 63 kcal/hr, P < .001, and 148 +/- 71 kcal/hr, P < .001, respectively). When examining movement with accelerometry, children moved significantly more than adults (55 +/- 5 arbitrary acceleration units and 23 +/- 2 arbitrary acceleration units, respectively, P < .001) while playing Nintendo Wii. CONCLUSION Activity-promoting video games have the potential to increase movement and energy expenditure in children and adults.

The Role of Free-Living Daily Walking in Human Weight Gain and Obesity

OBJECTIVE—Diminished daily physical activity explains, in part, why obesity and diabetes have become worldwide epidemics. In particular, chair use has replaced ambulation, so that obese individuals tend to sit for ∼2.5 h/day more than lean counterparts. Here, we address the hypotheses that free-living daily walking distance is decreased in obesity compared with lean subjects and that experimental weight gain precipitates decreased daily walking. RESEARCH DESIGN AND METHODS—During weight-maintenance feeding, we measured free-living walking using a validated system that captures locomotion and body movement for 10 days in 22 healthy lean and obese sedentary individuals. These measurements were then repeated after the lean and obese subjects were overfed by 1,000 kcal/day for 8 weeks. RESULTS—We found that free-living walking comprises many (∼47) short-duration (<15 min), low-velocity (∼1 mph) walking bouts. Lean subjects walked 3.5 miles/day more than obese subjects (n = 10, 10.3 ± 2.5 vs. n = 12, 6.7 ± 1.8 miles/day; P = 0.0009). With overfeeding, walking distance decreased by 1.5 miles/day compared with baseline values (−1.5 ± 1.7 miles/day; P = 0.0005). The decrease in walking that accompanied overfeeding occurred to a similar degree in the lean (−1.4 ± 1.9 miles/day; P = 0.04) and obese (−1.6 ± 1.7 miles/day; P = 0.008) subjects. CONCLUSIONS—Walking is decreased in obesity and declines with weight gain. This may represent a continuum whereby progressive increases in weight are associated with progressive decreases in walking distance. By identifying walking as pivotal in weight gain and obesity, we hope to add credence to an argument for an ambulatory future.

Changing the School Environment to Increase Physical Activity in Children

We examined the hypothesis that elementary school‐age children will be more physically active while attending school in a novel, activity‐permissive school environment compared to their traditional school environment. Twenty‐four children were monitored with a single‐triaxial accelerometer worn on the thigh. The students attended school in three different environments: traditional school with chairs and desks, an activity‐permissive environment, and finally their traditional school with desks which encouraged standing. Data from the school children were compared with another group of age‐matched children (n = 16) whose physical activity was monitored during summer vacation. When children attended school in their traditional environment, they moved an average (mean ± s.d.) of 71 ± 0.4 m/s2. When the children attended school in the activity‐permissive environment, they moved an average of 115 ± 3 m/s2. The children moved 71 ± 0.7 m/s2 while attending the traditional school with standing desks. Children moved significantly more while attending school in the activity‐permissive environment compared to the amount that they moved in either of the traditional school environments (P < 0.0001 for both). Comparing childrens activity while they were on summer vacation (113 ± 8 m/s2) to school‐bound children in their traditional environment showed significantly more activity for the children on summer vacation (P < 0.0001). The school children in the activity‐permissive environment were as active as children on summer vacation. Children will move more in an activity‐permissive environment. Strategies to increase the activity of school children may involve re‐designing the school itself.

Feasibility of a walking workstation to increase daily walking

Objective: The number of calories expended in the workplace has declined significantly in the past 75 years. A walking workstation that allows workers to walk while they work has the potential to increase caloric expenditure. We evaluated whether employees can and will use walking workstations while performing their jobs. Methods and procedures: We studied nurses, clinical assistants, secretaries and appointment secretaries using the StepWatch Activity Monitor System (which accurately measures steps taken at slow speeds) while performing their job functions in their usual fashion and while using the walking workstation. Results: Subjects increased the number of steps taken during the workday by 2000 steps per day (p<0.05). This was equivalent to an increase in caloric expenditure of 100 kcal/day. Subjects reported that they enjoyed using the workstation, that it could be used in the actual work arena and that, if available, they would use it. Discussion: Walking workstations have the potential for promoting physical activity and facilitating weight loss. Several subjects in this study expended more than 200 extra calories daily using such a system. Further trials are indicated.

Pedometer Accuracy for Children: Can We Recommend Them for Our Obese Population?

OBJECTIVE. In this study, we investigated the accuracy of measuring walking steps with commercially available pedometers and an accelerometer-based step-counter in normal and overweight children. Our primary hypothesis was that commercially available pedometers are not an accurate measure of walking steps in normal and overweight children while walking. Our secondary hypothesis was that the accelerometer-based step-counter provides an accurate measure of walking steps in normal and overweight children. METHODS. Thirteen boys (11 ± 1 years) and 14 girls (11 ± 1 years) who ranged in BMI from 15 to 27 kg/m2 (16 normal and 11 overweight or obese) were recruited. Each child wore 4 pedometers at the waist and 1 accelerometer-based step-counter on each ankle. Steps were manually counted and energy expenditure was measured while the child walked on the treadmill at 0.5, 1.0, 1.5, and 2.0 mph, each for 5 minutes. The step-counting devices were also validated while children walked on level ground at a self-selected pace. RESULTS. For the commercially available pedometers at the lowest speed of 0.5 mph, the percentage error approximated 100% for both of the pedometers. At the fastest speed of 2.0 mph, the percentage error approximated 60%. Conversely the accelerometer-based step-counter showed a percentage error of 24% ± 22% (mean ± SD) at 0.5 mph; however, as walking speed increased, the error declined to 5% ± 8% at 1.0 mph, 4% ± 5% at 1.5 mph, and 2% ± 2% at 2.0 mph. The relationship between steps counted and walking energy expenditure showed good linear correlation. CONCLUSIONS. Commercially available pedometers are less accurate for measuring walking and require discretion in their use for children. The accuracy of the accelerometer-based step-counter enables it to be used as a tool to assess and potentially promote physical activity in normal and overweight children.

A miniaturized low power personal motion analysis logger utilizing MEMS accelerometers and low power microcontroller

Portable uniaxial accelerometer units have been widely used to quantify non-exercise activity, but these instruments are not sufficiently sensitive to quantify the physical activity of a given free-living individual. Significant improvements can be made over current systems by utilizing cutting-edge technologies, such as low-power microcontrollers and MEMS accelerometers, to develop power-efficient motion analysis systems with small form factors. A motion tracking system with a very compact form factor would experience greatly expanded use and allow a wider range of activities to be studied. We have built several small devices incorporating either 2-axis or 3-axis MEMS accelerometer, low-power microcontroller, and mini secure digital memory card. Our device has proven to be comparable to the existing motion analysis system at distinguishing subject posture and motion.

Optimization of accelerometers for measuring walking

The devastating impact of obesity on global health is without question and it is generally agreed that low levels of physical activity, particularly sitting (i.e., sedentariness), are important in its pathogenesis. Therefore, the measure of physical activity such as walking is vital to its use in the research and clinical milieu. This study investigated three accelerometry parameters (sampling rate, range, and data depth) on ten healthy subjects (BMI 18–31 kg/m2) walking on a calibrated treadmill at eight speeds (0, 0.95, 1.74, 2.48, 3.22, 4.04, 4.83, and 5.70 km/h) while wearing a three-axis accelerometer on the thigh (Crossbow Technology, San Jose, CA) in order to find an optimal system for the determination of walking speed, as well as a new data analysis strategy using a differentiation of the acceleration values (jerk). Twenty-four sampling rates (2–25 Hz in 1-Hz intervals) and seven acceleration ranges (± 1 g – ± 2.5 g at 0.25-g intervals) were used to create a 24 × 7 factorial design. Data was also truncated from two to seven digits in the mantissa. This study found that although there is an improvement in walking speed prediction when sampling rate was set above 4 Hz (P<0.0002), there was no further improvement when the sampling rate is set higher. This study found that there is an increase in walking speed prediction accuracy when the range of acceleration is limited to ± 1 g (P<0.0024 for ± 2 g versus ± 1 g). This study found that increasing or decreasing data depth has no impact on walking speed prediction accuracy. Further, this study found that a model based on jerk was accurate at predicting walking speeds (r2 0.9 for all comparisons). For measuring walking using a sensor on the thigh, there is no significant improvement gained by large sampling rates, data ranges, or data precision. A model based on the time rate of change of acceleration is a valid analysis tool for measuring walking.