Levine, James A.

The goal of health care is to provide high-quality care at an affordable cost for its patients. However, the population it serves has changed dramatically since the popularization of hospital-based health care. With available new technology, alternative health care delivery methods can be designed and tested. This study examines scalable office-based health care for small business, where health care is delivered to the office floor. This delivery was tested in 18 individuals at a small business in Minneapolis, Minnesota. The goal was to deliver modular health care and mitigate conditions such as diabetes, hyperlipidaemia, obesity, sedentariness and metabolic disease. The modular health care system was welcomed by employees – 70% of those eligible enrolled. The findings showed that the modular health care deliverable was feasible and effective. The data demonstrated significant improvements in weight loss, fat loss and blood variables for at risk participants. This study leaves room for improvement and further innovation. Expansion to include offerings such as physicals, diabetes management, smoking cessation and prenatal treatment would improve its utility. Future studies could include testing the adaptability of delivery method, as it should adapt to reach rural and under-served populations.

Objective: This study aimed to determine the effects of active workstation designs on speed and error during typing, mouse pointing, and combined type and mouse-use tasks. Background: Office ergonomics has focused on musculoskeletal disorder prevention; however, increasing computer-based work also increases health risks associated with inactivity. Workstations allowing computer users to walk or cycle while performing computer tasks have been shown to demand sufficient energy expenditure to result in significant health benefits. However the performance effects of being active while using a computer have not been documented. Method: Thirty office workers (16 female, 15 touch typists) performed standardized computer tasks in six workstation conditions: sitting, standing, walking at 1.6 km/h and 3.2 km/h, and cycling at 5 and 30 watts. Performance, perceived performance, and heart rate were measured. Results: Computer task performance was lower when walking and slightly lower when cycling, compared with chair sitting. Standing performance was not different from sitting performance. Mouse performance was more affected than typing performance. Performance decrements were equal for females and males and for touch typists and nontouch typists. Conclusion: Performance decrements maybe related to both biomechanical and cognitive processes. Active workstations may be less suitable for mouse-intensive work and susceptible users. Application: Although active workstations may result in some decrement in performance, their ability to increase daily energy expenditure may make them a feasible solution for workplace inactivity.

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.

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/m 2 ) 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 ( r 2 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.

This article describes research that aims to encourage physical activity through a novel pervasive gaming paradigm. Data from a wearable accelerometer are logged wirelessly to a cell phone and control the animation of an avatar that represents the player in a virtual race game with other players over the cellular network. Winners are declared every day and players with an excess of activity points can spend some to get hints in mental games of the suite, like Sudoku. The racing game runs in the background throughout the day and every little move counts. As the gaming platform is embedded in the daily routine of players, it may act as a strong behavioral modifier and increase everyday physical activity other than volitional sporting exercise. Such physical activity (e.g., taking the stairs), is termed NEAT and was shown to play a major role in obesity prevention and intervention. A pilot experiment demonstrates that players are engaged in NEAT-o-Games and become more physically active while having a good dosage of fun.