Low-Cost, Scalable Classroom-Based Approach to Promoting Physical Activity in Preschool Children.

BACKGROUND: This study examined the impact of short activity breaks in preschool children. The hypotheses were that preschool children receiving three five-minute activity breaks per day would increase (a) school time physical activity and (b) education scores compared to a control group not receiving the intervention. METHODS: For 8 weeks, the Intervention Group (n = 13) incorporated three 5-minute activity breaks into their classroom time while the Control Group (n = 12) did not incorporate the activity breaks. Physical activity was measured using a triaxial accelerometer. Education was assessed using standardized methods. FINDINGS: After 8 weeks, the preschool children in the Intervention Group increased their school time physical activity from 11,641 ± (SD) 1,368 Acceleration Units (AU)/ hour to 16,058 ± 2,253 AU/hour (P < 0.001). The children in the control group did not increase their physical activity (11,379 ± 2,427 cf 11,624 ± 2,441; ns). Students in the Intervention Group improved their education scores more than students in the control group (18 ± 12 cf 8 ± 7 points, P = 0.01); Letter Recognition improved in particular (9 ± 6 cf 2 ± 4 points, P = 0.001). CONCLUSIONS: The incorporation of three 5-minute activity breaks was associated with increased school time physical activity and improved learning.

Low-cost and Scalable Classroom Equipment to Promote Physical Activity and Improve Education.

BACKGROUND: We tested a low-cost and scalable set of classroom equipment, called Active Classroom Equipment, which was designed to promote physical activity while children learn. We hypothesized the Active Classroom Equipment would be associated with increased physical activity without impairing learning. METHODS: Fourteen first-grade students in a public elementary school (7 females, 7 males, aged 6.9 ± (SD) 0.4 years, 24 ± 5.4 kg, BMI 15.8 ± 2.6 kg/m2) used the Active Classroom Equipment for 30 minutes each day throughout the school year. Five-day physical activity was measured using validated triaxial accelerometers at baseline (before the intervention began) and on 4 sequential occasions during the 9-month intervention. RESULTS: For the baseline period, 5-day physical activity averaged 157 ± 65 AU/min. When the 14 children accessed the Active Classroom Equipment, their mean 5-day physical activity was 229 ± 103 Acceleration Units (AU)/ min (P < .0001). There were sequential increases in physical activity over the 9-month intervention (Quarter 1: 163 ± 94 AU/min, Quarter 2: 227 ± 108 AU/min, Quarter 3: 278 ± 61 AU/min, Quarter 4: 305 ± 65 AU/min). Students' Dynamic Indicators of Basic Early Literacy Skills (DIBELS) scores improved. CONCLUSION: Active Classroom Equipment may be one approach to increase physical activity.

Comparison of physical activity sensors and heart rate monitoring for real-time activity detection in type 1 diabetes and control subjects.

BACKGROUND: Currently, patients with type 1 diabetes decide on the amount of insulin to administer based on several factors, including current plasma glucose value, expected meal input, and physical activity (PA). One future therapeutic modality for patients with type 1 diabetes is the artificial endocrine pancreas (AEP). Incorporation of PA could enhance the efficacy of AEP significantly. We compared the main technologies used for PA quantitation. SUBJECTS AND METHODS: Data were collected during inpatient studies involving healthy control subjects and type 1 diabetes. We report PA quantified from accelerometers (acceleration units [AU]) and heart rate (HR) monitors during a standardized activity protocol performed after a dinner meal at 7 p.m. from nine control subjects (four were males, 37.4±12.7 years old, body mass index of 24.8±3.8 kg/m(2), and fasting plasma glucose of 4.71±0.63 mmol/L) and eight with type 1 diabetes (six were males, 45.2±13.4 years old, body mass index of 25.1±2.9 kg/m(2), and fasting plasma glucose of 8.44±2.31 mmol/L). RESULTS: The patient-to-patient variability was considerably less when examining AU compared with HR monitors. Furthermore, the exercise bouts and rest periods were more evident from the data streams when AUs were used to quantify activity. Unlike the AU, the HR measurements provided little insight for active and rest stages, and HR data required patient-specific standardizations to discern any meaningful pattern in the data. CONCLUSIONS: Our results indicated that AU provides a reliable signal in response to PA, including low-intensity activity. Correlation of this signal with continuous glucose monitoring data would be the next step before exploring inclusion as input for AEP control.

Physical activity measured by physical activity monitoring system correlates with glucose trends reconstructed from continuous glucose monitoring.

BACKGROUND: In type 1 diabetes mellitus (T1DM), physical activity (PA) lowers the risk of cardiovascular complications but hinders the achievement of optimal glycemic control, transiently boosting insulin action and increasing hypoglycemia risk. Quantitative investigation of relationships between PA-related signals and glucose dynamics, tracked using, for example, continuous glucose monitoring (CGM) sensors, have been barely explored. SUBJECTS AND METHODS: In the clinic, 20 control and 19 T1DM subjects were studied for 4 consecutive days. They underwent low-intensity PA sessions daily. PA was tracked by the PA monitoring system (PAMS), a system comprising accelerometers and inclinometers. Variations on glucose dynamics were tracked estimating first- and second-order time derivatives of glucose concentration from CGM via Bayesian smoothing. Short-time effects of PA on glucose dynamics were quantified through the partial correlation function in the interval (0, 60 min) after starting PA. RESULTS: Correlation of PA with glucose time derivatives is evident. In T1DM, the negative correlation with the first-order glucose time derivative is maximal (absolute value) after 15 min of PA, whereas the positive correlation is maximal after 40-45 min. The negative correlation between the second-order time derivative and PA is maximal after 5 min, whereas the positive correlation is maximal after 35-40 min. Control subjects provided similar results but with positive and negative correlation peaks anticipated of 5 min. CONCLUSIONS: Quantitative information on correlation between mild PA and short-term glucose dynamics was obtained. This represents a preliminary important step toward incorporation of PA information in more realistic physiological models of the glucose-insulin system usable in T1DM simulators, in development of closed-loop artificial pancreas control algorithms, and in CGM-based prediction algorithms for generation of hypoglycemic alerts.

Treadmill desks: A 1-year prospective trial.

OBJECTIVE: Sedentariness is associated with weight gain and obesity. A treadmill desk is the combination of a standing desk and a treadmill that allow employees to work while walking at low speed. DESIGN AND METHODS: The hypothesis was that a 1-year intervention with treadmill desks is associated with an increase in employee daily physical activity (summation of all activity per minute) and a decrease in daily sedentary time (zero activity). Employees (n = 36; 25 women, 11 men) with sedentary jobs (87 ± 27 kg, BMI 29 ± 7 kg/m(2) , n = 10 Lean BMI < 25 kg/m(2) , n = 15 Overweight 25 < BMI < 30 kg/m(2) , n = 11 Obese BMI > 30 kg/m(2) ) volunteered to have their traditional desk replaced with a treadmill desk to promote physical activity for 1 year. RESULTS: Daily physical activity (using accelerometers), work performance, body composition, and blood variables were measured at Baseline and 6 and 12 months after the treadmill desk intervention. Subjects who used the treadmill desk increased daily physical activity from baseline 3,353 ± 1,802 activity units (AU)/day to, at 6 months, 4,460 ± 2,376 AU/day (P < 0.001), and at 12 months, 4,205 ± 2,238 AU/day (P < 0.001). Access to the treadmill desks was associated with significant decreases in daily sedentary time (zero activity) from at baseline 1,020 ± 75 min/day to, at 6 months, 929 ± 84 min/day (P < 0.001), and at 12 months, 978 ± 95 min/day (P < 0.001). For the whole group, weight loss averaged 1.4 ± 3.3 kg (P < 0.05). Weight loss for obese subjects was 2.3 ± 3.5 kg (P < 0.03). Access to the treadmill desks was associated with increased daily physical activity compared to traditional chair-based desks; their deployment was not associated with altered performance. For the 36 participants, fat mass did not change significantly, however, those who lost weight (n = 22) lost 3.4 ± 5.4 kg (P < 0.001) of fat mass. Weight loss was greatest in people with obesity. CONCLUSIONS: Access to treadmill desks may improve the health of office workers without affecting work performance.

The effect of walking on postprandial glycemic excursion in patients with type 1 diabetes and healthy people.

OBJECTIVE: Physical activity (PA), even at low intensity, promotes health and improves hyperglycemia. However, the effect of low-intensity PA captured with accelerometery on glucose variability in healthy individuals and patients with type 1 diabetes has not been examined. Quantifying the effects of PA on glycemic variability would improve artificial endocrine pancreas (AEP) algorithms. RESEARCH DESIGN AND METHODS: We studied 12 healthy control subjects (five males, 37.7 ± 13.7 years of age) and 12 patients with type 1 diabetes (five males, 37.4 ± 14.2 years of age) for 88 h. Participants performed PA approximating a threefold increase over their basal metabolic rate. PA was captured using a PA-monitoring system, and interstitial fluid glucose concentrations were captured with continuous glucose monitors. In random order, one meal per day was followed by inactivity, and the other meals were followed by walking. Glucose and PA data for a total of 216 meals were analyzed from 30 min prior to meal ingestion to 270 min postmeal. RESULTS: In healthy subjects, the incremental glucose area under the curve was 4.5 mmol/L/270 min for meals followed by walking, whereas it was 9.6 mmol/L/270 min (P = 0.022) for meals followed by inactivity. The corresponding glucose excursions for those with type 1 diabetes were 7.5 mmol/L/270 min and 18.4 mmol/L/270 min, respectively (P < 0.001). CONCLUSIONS: Walking significantly impacts postprandial glucose excursions in healthy populations and in those with type 1 diabetes. AEP algorithms incorporating PA may enhance tight glycemic control end points.

Internal medicine residents' computer use in the inpatient setting.

BACKGROUND: Studies have suggested that patient contact time for internal medicine residents is decreasing and being replaced with computer-related activities, yet objective data regarding computer use by residents are lacking. OBJECTIVE: The aim of this study was to objectively measure time use by internal medicine residents while on duty in the hospital setting using real-time, voice-capture technology. METHODS: First- and third-year categoric internal medicine residents participated (n  =  25) during a 3-month period in 2010 while rotating on general internal medicine rotations. Portable speech-recognition technology was used to record residents' activities. The residents were prompted every 15 minutes from an earpiece and asked to categorize the activity they had been doing since the last prompt, choosing from a predetermined list of 15 activities. RESULTS: Of the 1008 duty-time responses, 493 (49%) were classified as computer-related activities, whereas 341 (34%) were classified as direct patient care, 110 (11%) were classified as noncomputer-related education, and 64 (6%) were classified as other activities. Of resident reported computer-use time, 70% was spent on patient notes and order entry. CONCLUSIONS: The results of our study suggest that computer use is the predominant activity for internal medicine residents while in the inpatient setting. Work redesign because of duty hour regulations should consider how to free up residents' time from computer-based activities to allow residents to engage in more direct patient care and noncomputer-based learning.

Scalable office-based health care.

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.

An accelerometer-based earpiece to monitor and quantify physical activity.

BACKGROUND: Physical activity is important in ill-health. Inexpensive, accurate and precise devices could help assess daily activity. We integrated novel activity-sensing technology into an earpiece used with portable music-players and phones; the physical-activity-sensing earpiece (PASE). Here we examined whether the PASE could accurately and precisely detect physical activity and measure its intensity and thence predict energy expenditure. METHODS: Experiment 1: 18 subjects wore PASE with different body postures and during graded walking. Energy expenditure was measured using indirect calorimetry. Experiment 2: 8 subjects wore the earpiece and walked a known distance. Experiment 3: 8 subjects wore the earpiece and 'jogged' at 3.5 mph. RESULTS: The earpiece correctly distinguished lying from sitting/standing and distinguished standing still from walking (76/76 cases). PASE output showed excellent sequential increases with increased in walking velocity and energy expenditure (r2 > .9). The PASE prediction of free-living walking velocity was, 2.5 +/- (SD) 0.18 mph c.f. actual velocity, 2.5 +/- 0.16 mph. The earpiece successfully distinguished walking at 3.5 mph from 'jogging' at the same velocity (P < .001). CONCLUSIONS: The subjects tolerated the earpiece well and were comfortable wearing it. The PASE can therefore be used to reliably monitor free-living physical activity and its associated energy expenditure.

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/s(2). When the children attended school in the activity-permissive environment, they moved an average of 115 +/- 3 m/s(2). The children moved 71 +/- 0.7 m/s(2) 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 children's activity while they were on summer vacation (113 +/- 8 m/s(2)) 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.

Measurement of daily activity in restrictive type anorexia nervosa.

OBJECTIVE: The assessment of daily activity in patients with restrictive type anorexia nervosa is limited by an absence of accurate and precise technology. We wanted to test a daily activity detecting device named, the physical activity monitoring system (PAMS). METHOD: Women participants with restrictive type anorexia nervosa (n = 8, 36 +/- 11 years, 17 +/- 2 kg/m(2)) and healthy women participants (n = 8, 30 +/- 11 years, 27 +/- 7 kg/m(2)) were asked to lie, sit, and stand motionless, and walk at 0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 mph while wearing PAMS. RESULTS: For all restrictive type anorexia nervosa and healthy participants, body posture was correctly detected for all measurements (300/300). There was excellent correlation of an individual's body acceleration with walking velocity and walking energy expenditure (r(2) > .99). CONCLUSION: The PAMS technology could serve as a tool for lending insight into the pathophysiology of restrictive type anorexia nervosa; and potentially measuring compliance with activity recommendations for medical professionals treating individuals with restrictive type anorexia nervosa.

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 approximately 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 (approximately 47) short-duration (<15 min), low-velocity ( approximately 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.

An office-place stepping device to promote workplace physical activity.

OBJECTIVE: It was proposed that an office-place stepping device is associated with significant and substantial increases in energy expenditure compared to sitting energy expenditure. The objective was to assess the effect of using an office-place stepping device on the energy expenditure of lean and obese office workers. METHODS: The office-place stepping device is an inexpensive, near-silent, low-impact device that can be housed under a standard desk and plugged into an office PC for self-monitoring. Energy expenditure was measured in lean and obese subjects using the stepping device and during rest, sitting and walking. 19 subjects (27+/-9 years, 85+/-23 kg): 9 lean (BMI<25 kg/m2) and 10 obese (BMI>29 kg/m2) attended the experimental office facility. Energy expenditure was measured at rest, while seated in an office chair, standing, walking on a treadmill and while using the office-place stepping device. RESULTS: The office-place stepping device was associated with an increase in energy expenditure above sitting in an office chair by 289+/-102 kcal/hour (p<0.001). The increase in energy expenditure was greater for obese (335+/-99 kcal/hour) than for lean subjects (235+/-80 kcal/hour; p = 0.03). The increments in energy expenditure were similar to exercise-style walking. CONCLUSION: The office-place stepping device could be an approach for office workers to increase their energy expenditure. If the stepping device was used to replace sitting by 2 hours per day and if other components of energy balance were constant, weight loss of 20 kg/year could occur.

Precision and accuracy of an ankle-worn accelerometer-based pedometer in step counting and energy expenditure.

BACKGROUND: Walking is a widely used approach to increase physical activity levels in obese patients. In this paper, we investigate the precision and accuracy of an ankle-worn dual-axis accelerometer (Stepwatch) and investigate its potential application as a predictor of energy expenditure. METHODS: Twenty healthy subjects (10 lean, 10 obese) wore spring-levered (Accusplit), piezoelectric (Omron HF-100), and Stepwatch pedometers. Subjects walked on a treadmill at 1, 2, and 3 mph and in a hallway at 1 and 1.85 mph, during which energy expenditure was measured. RESULTS: The Stepwatch counted 99.7 +/- 0.67% (mean +/- SEM) of the manual counts. In comparison, the Omron pedometer counted 61 +/- 3.3% and the Accusplit counted 26 +/- 2.8% of the manual counts at 1 mph although all pedometers were accurate (> 98% of counts) at 3 mph. In repeated measures, the Stepwatch produced negligible variance (SD = 0.36) over all speed whereas the other pedometers showed a large amount of variance at all speed (SD = 4-13). Stepwatch counts were predictive of walking energy expenditure corrected by weight (r2 > 0.8). CONCLUSION: The counts from the Stepwatch were virtually identical to the manual counts from a trained investigator and provided a reliable predictor of walking energy expenditure.