Short-Term Housing in Metabolic Caging on Measures of Energy and Fluid Balance in Male C57BL/6J Mice (Mus musculus)

Metabolic caging is an important tool for quantitative urine and feces collection in rodents, although significant limitationsand problems accompany its use. Despite strong opinions among investigators regarding the effects of metabolic caging onenergy and fluid homeostasis, careful quantitative analysis of the impact of this caging type—particularly when used for mice—is lacking. The current study assessed the effects of metabolic caging, with or without modifications such as plastic platform inserts, on ingestive behaviors, energy expenditure, accuracy of urine and fecal collection, and ambulatory activities in male C57BL/6J mice. Housing mice in metabolic cages, regardless of platform inclusion, increased energy expenditure without modifying food intake, presumably due to the inability of mice to perform normal thermoregulatory behaviors (burrowing and huddling). Surprisingly, mice in metabolic cages actively avoided platforms, and the inclusion of platforms modified the behavior of the mice and had position-dependent effects that reduced the accuracy of urine collection. Moving mice from cohousing to individual housing in home cages also increased ingestive behaviors and energy expenditure. We conclude that single housing of male C57BL/6J mice increases energy expenditure, that this increase is potentiated in metaboliccaging conditions, and that platforms in metabolic cages alter mouse behavior and urine collection. Additional future workis needed to determine the potential benefits of using higher ambient temperature for studies of mice in metabolic cagingand whether the above effects occur in females and other strains of mice and other rodent species.

Adaptive Accumulation of Plantar Pressure for Ambulatory Activity Recognition and Pedestrian Identification

In this paper, we propose a novel method for ambulatory activity recognition and pedestrian identification based on temporally adaptive weighting accumulation-based features extracted from categorical plantar pressure. The method relies on three pressure-related features, which are calculated by accumulating the pressure of the standing foot in each step over three different temporal weighting forms. In addition, we consider a feature reflecting the pressure variation. These four features characterize the standing posture in a step by differently weighting step pressure data over time. We use these features to analyze the standing foot during walking and then recognize ambulatory activities and identify pedestrians based on multilayer multiclass support vector machine classifiers. Experimental results show that the proposed method achieves 97% accuracy for the two tasks when analyzing eight consecutive steps. For faster processing, the method reaches 89.9% and 91.3% accuracy for ambulatory activity recognition and pedestrian identification considering two consecutive steps, respectively, whereas the accuracy drops to 83.3% and 82.3% when considering one step for the respective tasks. Comparative results demonstrated the high performance of the proposed method regarding accuracy and temporal sensitivity.

Physical Activity and Sedentary Behavior Assessment: A Laboratory-Based Evaluation of Agreement between Commonly Used ActiGraph and Omron Accelerometers

Different models of accelerometer have the potential to provide a different estimate of the same physical activity or sedentary behavior. Our study compared the outputs of the Active Style Pro (ASP) and ActiGraph (AG) devices in assessing predicted metabolic equivalents (METs) for specific activities under laboratory conditions. Thirty healthy young adults wore two hip accelerometers (ASP and AG), simultaneously while performing twenty-two activities (eight sedentary, eight household, and six ambulatory activities) in a controlled laboratory setting. For the AG, predicted METs for each activity was calculated using four equations based on vertical-axis and vector magnitude data. Separate paired t-tests and Bland–Altman analysis examined the difference and agreement in METs between AG using four commonly used equations and ASP measurements for each activity. AG devices using different equations calculated significantly different outcomes for most activities compared with ASP devices. The smallest differences in predicted METs estimates between ASP and AG were observed for ambulatory activities. Ambulatory activities demonstrated the best agreement between ASP and AG regardless of which AG equation was used. Our findings can be used to assist researchers in their selection of accelerometer and output estimation equations for measuring physical activity and sedentary behavior in adults.

Prediction of Physical Activity Intensity with Accelerometry in Young Children

Background: An algorithm for the classification of ambulatory and non-ambulatory activities using the ratio of unfiltered to filtered synthetic acceleration measured with a triaxial accelerometer and predictive models for physical activity intensity (METs) in adults and in elementary school children has been developed. The purpose of the present study was to derive predictive equations for METs with a similar algorithm in young children. Methods: Thirty-seven healthy Japanese children (four- to six-years old) participated in this study. The five non-ambulatory activities including low-intensity activities, and five ambulatory activities were selected. The raw accelerations using a triaxial accelerometer and energy expenditure by indirect calorimetry using the Douglas bag method during each activity were collected. Results: For non-ambulatory activities, especially light-intensity non-ambulatory activities, linear regression equations with a predetermined intercept (0.9) or quadratic equations were a better fit than the linear regression. The equations were different from those for adults and elementary school children. On the other hand, the ratios of unfiltered to filtered synthetic acceleration in non-ambulatory activities were different from those in ambulatory activities, as in adults and elementary school children. Conclusions: Our calibration model for young children could accurately predict intensity of physical activity including low-intensity non-ambulatory activities.

Activity Classification Feasibility Using Wearables: Considerations for Hip Fracture

Falls in the elderly are a common health issue that can involve severe injuries like hip fractures, requiring considerable medical attention, and subsequent care. Following surgery, physiotherapy is essential for strengthening muscles, mobilizing joints and fostering the return to physical activities. Ideally, physiotherapy programmes would benefit from active home-based monitoring of the elderly patients’ daily activities and exercises. This paper aims at providing a preliminary analysis addressing three key research questions. First, what are the key involved activities (at-hospital, home exercises, and activities of daily living) during the post-operative hip fracture rehabilitation process? Second, how can one monitor and identify a range of leg exercises accurately? Last, what is the most suitable sensor location that can categorize the majority of the physical activities thought to be important during the rehabilitation programme? During preliminary testing, it was noted that a standard deviation of the acceleration signal was suitable for classification of static activities like sitting, whereas classification of the ambulatory activities like walking, both the frequency content and related amplitude of the acceleration signal, plays a significant role. The research findings suggest that the ankle is an appropriate location for monitoring most of the leg movement physical activities.