Chinmay U. Manohar

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.

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.

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.

Face Recognition Beyond the Visible Spectrum

The facial vascular network is highly characteristic to the individual, much like the way his fingerprint is. A non-obtrusive way to capture this information is through thermal imaging. The convective heat transfer effect from the flow of “hot” arterial blood in superficial vessels creates characteristic thermal imprints, which are at a gradient with the surrounding tissue. This casts sigmoid edges on the human tissue where major blood vessels are present. We present an algorithmic methodology to extract and represent the facial vasculature. The methodology combines image morphology and probabilistic inference. The morphology captures the overall structure of the vascular network while the probabilistic part reflects the positional uncertainty for the vessel walls, due to the phenomenon of thermal diffusion. The accuracy of the methodology is tested through extensive experimentation and meticulous ground-truthing. Furthermore, the efficacy of this information for identity recognition is tested on substantial databases.

Evaluation of the Accuracy of a Triaxial Accelerometer Embedded into a Cell Phone Platform for Measuring Physical Activity

BACKGROUND Physical activity is important in health and weight management. Several cell phone platforms integrate an accelerometer onto the motherboard. Here we tested the validity of the cell phone accelerometer to assess physical activity in a controlled laboratory setting. METHODS 31 subjects wore the cell phone on their waist along with the validated Physical Activity Monitoring System (PAMS) with different body postures and during graded walking. Energy expenditure was measured using indirect calorimetry. 11 subjects also wore the iPhone at different locations such as arm, hand, pant pocket, etc. RESULTS The cell phone accelerometer was accurate and precise compared to the PAMS, with an intra-class correlation coefficient (r2> 0.98). The cell phone accelerometer showed excellent sequential increases with increased in walking velocity and energy expenditure (r2>0.9). CONCLUSION An accelerometer embedded into a cell phone was accurate and reliable in measuring and quantifying physical activity in the laboratory setting. Data from free-living users shows promise for deployment of a comprehensive integrated physical activity promoting and weight loss platform using such mobile technologies.

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.

A Stand-Alone Accelerometer System for Free-Living Individuals to Measure and Promote Physical Activity

Background.Physical activity is important for multiple aspects of health, for example, cancer prevention, metabolic disease treatment, cardiovascular health, and obesity management. Despite the improved capability of measuring physical activity in the research arena, the options are scarce and less reliable for measurements in free-living people. In this article, the authors tested the validity of a robust stand-alone patient-operated Accelerometer System that can measure physical activity and sedentariness in active people.Methods.Thirty subjects wore the Accelerometer System along with the validated physical activity monitoring system (PAMS) with different body postures and during graded walking at 7 velocities. Energy expenditure was measured using indirect calorimetry.Results.In all the 30 subjects, the Accelerometer System distinguished sedentary and walking activity reliably even with ½ mph increments in walking and was accurate and precise compared with PAMS, with an intraclass correlation coeffici...

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.

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.