155 research outputs found
A Comparison of Wrist and Hip Accelerometer Output at Different Walking Speeds
Physical activity has been objectively measured using hip-worn accelerometers for decades. However, wrist-worn accelerometers are currently used in large-scale studies. Differences in wrist and hip dynamics during locomotion may affect monitor output, which may impact how prediction models are built. PURPOSE: To compare ActiGraph™ wrist and hip accelerations (g’s) at varying locomotion speeds. METHODS: Participants (N = 7) wore ActiGraph™ GT3X+ accelerometers on the dominant wrist and hip (sampling rate 80Hz). They performed three 5-minute trials at self-paced (SP), slow (SL), and fast (F) over-ground walking speeds. Mean and standard deviation of the vector magnitude (VM) were calculated from two 20-s data windows per condition. Linear mixed-effects models were used to determine if the relationship was different between speed and vector VM at the hip and wrist. RESULTS: Significant differences were found between the slopes (speed vs VM) of the hip m = 0.052 (95% CI: 0.033, 0.103) compared to the wrist m = 0.195 (95% CI: 0.160, 0.230) p\u3c0.001. DISCUSSION: The results show that ActiGraph™ wrist and hip accelerations (g’s) differ at varying locomotion speeds. There is a curvilinear increase in VM at the wrist as locomotion speed increases, whereas there is a linear increase in VM at the hip as locomotion speed increases. The pattern of change of wrist VM is different and more variable between subjects compared to hip VM, which may impact measurement error and model development. Additionally, wrist VM is more responsive to changes in speed than hip VM, suggesting that a wrist worn accelerometer may be more sensitive to locomotion intensity
Do linden trees kill bees? Reviewing the causes of bee deaths on silver linden (Tilia tomentosa)
For decades, linden trees (basswoods or lime trees), and particularly silver linden (Tilia tomentosa), have been linked to mass bee deaths. This phenomenon is often attributed to the purported occurrence of the carbohydrate mannose, which is toxic to bees, in Tilia nectar. In this review, however, we conclude that from existing literature there is no experimental evidence for toxicity to bees in linden nectar. Bee deaths on Tilia probably result from starvation, owing to insufficient nectar resources late in the tree's flowering period. We recommend ensuring sufficient alternative food sources in cities during late summer to reduce bee deaths on silver linden. Silver linden metabolites such as floral volatiles, pollen chemistry and nectar secondary compounds remain underexplored, particularly their toxic or behavioural effects on bees. Some evidence for the presence of caffeine in linden nectar may mean that linden trees can chemically deceive foraging bees to make sub-optimal foraging decisions, in some cases leading to their starvation
Validation of the use of Actigraph GT3X accelerometers to estimate energy expenditure in full time manual wheel chair users with Spinal Cord Injury
Study design: Cross-sectional validation study.
Objectives: The goals of this study were to validate the use of accelerometers by means of multiple linear models (MLMs) to estimate the O2 consumption (VO2) in paraplegic persons and to determine the best placement for accelerometers on the human body.
Setting: Non-hospitalized paraplegics’ community.
Methods: Twenty participants (age=40.03 years, weight=75.8 kg and height=1.76 m) completed sedentary, propulsion and housework activities for 10 min each. A portable gas analyzer was used to record VO2. Additionally, four accelerometers (placed on the non-dominant chest, non-dominant waist and both wrists) were used to collect second-by-second acceleration signals. Minute-by-minute VO2 (ml kg−1 min−1) collected from minutes 4 to 7 was used as the dependent variable. Thirty-six features extracted from the acceleration signals were used as independent variables. These variables were, for each axis including the resultant vector, the percentiles 10th, 25th, 50th, 75th and 90th; the autocorrelation with lag of 1 s and three variables extracted from wavelet analysis. The independent variables that were determined to be statistically significant using the forward stepwise method were subsequently analyzed using MLMs.
Results: The model obtained for the non-dominant wrist was the most accurate (VO2=4.0558−0.0318Y25+0.0107Y90+0.0051YND2−0.0061ZND2+0.0357VR50) with an r-value of 0.86 and a root mean square error of 2.23 ml kg−1 min−1.
Conclusions: The use of MLMs is appropriate to estimate VO2 by accelerometer data in paraplegic persons. The model obtained to the non-dominant wrist accelerometer (best placement) data improves the previous models for this population.LM Garcia-Raffi and EA Sanchez-Perez gratefully acknowledge the support of the Ministerio de Economia y Competitividad under project #MTM2012-36740-c02-02. X Garcia-Masso is a Vali + D researcher in training with support from the Generalitat Valenciana.Garcia Masso, X.; Serra Añó, P.; García Raffi, LM.; Sánchez Pérez, EA.; Lopez Pascual, J.; González, L. (2013). Validation of the use of Actigraph GT3X accelerometers to estimate energy expenditure in full time manual wheel chair users with Spinal Cord Injury. Spinal Cord. 51(12):898-903. https://doi.org/10.1038/sc.2013.85S8989035112Van den Berg-Emons RJ, Bussmann JB, Haisma JA, Sluis TA, van der Woude LH, Bergen MP et al. A prospective study on physical activity levels after spinal cord injury during inpatient rehabilitation and the year after discharge. Arch Phys Med Rehabil 2008; 89: 2094–2101.Jacobs PL, Nash MS . Exercise recommendations for individuals with spinal cord injury. Sports Med 2004; 34: 727–751.Erikssen G . Physical fitness and changes in mortality: the survival of the fittest. Sports Med 2001; 31: 571–576.Warburton DER, Nicol CW, Bredin SSD . Health benefits of physical activity: the evidence. CMAJ 2006; 174: 801–809.Haennel RG, Lemire F . Physical activity to prevent cardiovascular disease. How much is enough? Can Fam Physician 2002; 48: 65–71.Manns PJ, Chad KE . Determining the relation between quality of life, handicap, fitness, and physical activity for persons with spinal cord injury. Arch Phys Med Rehabil 1999; 80: 1566–1571.Hetz SP, Latimer AE, Buchholz AC, Martin Ginis KA . Increased participation in activities of daily living is associated with lower cholesterol levels in people with spinal cord injury. Arch Phys Med Rehabil 2009; 90: 1755–1759.Buchholz AC, Martin Ginis KA, Bray SR, Craven BC, Hicks AL, Hayes KC et al. Greater daily leisure time physical activity is associated with lower chronic disease risk in adults with spinal cord injury. Appl Physiol Nutr Metab 2009; 34: 640–647.Slater D, Meade MA . Participation in recreation and sports for persons with spinal cord injury: review and recommendations. Neurorehabilitation 2004; 19: 121–129.Valanou EM, Bamia C, Trichopoulou A . Methodology of physical-activity and energy-expenditure assessment: a review. J Public Health 2006; 14: 58–65.Liu S, Gao RX, Freedson PS . Computational methods for estimating energy expenditure in human physical activities. Med Sci Sports Exerc 2012; 44: 2138–2146.Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M . Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 2008; 40: 181–188.Riddoch CJ, Bo Andersen L, Wedderkopp N, Harro M, Klasson-Heggebø L, Sardinha LB et al. Physical activity levels and patterns of 9- and 15-yr-old European children. Med Sci Sports Exerc 2004; 36: 86–92.Hiremath SV, Ding D . Evaluation of activity monitors in manual wheelchair users with paraplegia. J Spinal Cord Med 2011; 34: 110–117.Hiremath SV, Ding D . Evaluation of activity monitors to estimate energy expenditure in manual wheelchair users. Conf Proc IEEE Eng Med Biol Soc 2009; 2009: 835–838.Washburn R, Copay A . Assessing physical activity during wheelchair pushing: validity of a portable accelerometer. Adapt Phys Activ Q 1999; 16: 290–299.Hiremath SV, Ding D . Regression equations for RT3 activity monitors to estimate energy expenditure in manual wheelchair users. Conf Proc IEEE Eng Med Biol Soc 2011; 2011: 7348–7351.Hiremath SV, Ding D, Farringdon J, Cooper RA . Predicting energy expenditure of manual wheelchair users with spinal cord injury using a multisensor-based activity monitor. Arch Phys Med Rehabil 2012; 93: 1937–1943.Bassett DR Jr, Ainsworth BE, Swartz AM, Strath SJ, O’Brien WL, King GA . Validity of four motion sensors in measuring moderate intensity physical activity. Med Sci Sports Exerc 2000; 32: S471–S480.Motl RW, Sosnoff JJ, Dlugonski D, Suh Y, Goldman M . Does a waist-worn accelerometer capture intra- and inter-person variation in walking behavior among persons with multiple sclerosis? Med Eng Phys 2010; 32: 1224–1228.Van Remoortel H, Raste Y, Louvaris Z, Giavedoni S, Burtin C, Langer D et al. Validity of six activity monitors in chronic obstructive pulmonary disease: a comparison with indirect calorimetry. PLoS One 2012; 7: e39198.Macfarlane DJ . Automated metabolic gas analysis systems: a review. Sports Med 2001; 31: 841–861.Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P . An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. J Appl Physiol 2009; 107: 1300–1307.Daubechies I . Ten Lectures on Wavelets. SIAM, Philadelphia. 1999.Debnat I . Wavelets and Signal Processing. Birkhauser, Boston. 2003.Collins EG, Gater D, Kiratli J, Butler J, Hanson K, Langbein WE . Energy cost of physical activities in persons with spinal cord injury. Med Sci Sports Exerc 2010; 42: 691–700.Lee M, Zhu W, Hedrick B, Fernhall B . Determining metabolic equivalent values of physical activities for persons with paraplegia. Disabil Rehabil 2010; 32: 336–343.Crouter SE, Clowers KG, Bassett DR Jr . A novel method for using accelerometer data to predict energy expenditure. J Appl Physiol 2006; 100: 1324–1331
Prediction of Bodyweight and Energy Expenditure Using Point Pressure and Foot Acceleration Measurements
Bodyweight (BW) is an essential outcome measure for weight management and is also a major predictor in the estimation of daily energy expenditure (EE). Many individuals, particularly those who are overweight, tend to underreport their BW, posing a challenge for monitors that track physical activity and estimate EE. The ability to automatically estimate BW can potentially increase the practicality and accuracy of these monitoring systems. This paper investigates the feasibility of automatically estimating BW and using this BW to estimate energy expenditure with a footwear-based, multisensor activity monitor. The SmartShoe device uses small pressure sensors embedded in key weight support locations of the insole and a heel-mounted 3D accelerometer. Bodyweight estimates for 9 subjects are computed from pressure sensor measurements when an automatic classification algorithm recognizes a standing posture. We compared the accuracy of EE prediction using estimated BW compared to that of using the measured BW. The results show that point pressure measurement is capable of providing rough estimates of body weight (root-mean squared error of 10.52 kg) which in turn provide a sufficient replacement of manually-entered bodyweight for the purpose of EE prediction (root-mean squared error of 0.7456 METs vs. 0.6972 METs). Advances in the pressure sensor technology should enable better accuracy of body weight estimation and further improvement in accuracy of EE prediction using automatic BW estimates
Modelling a response as a function of high frequency count data: the association between physical activity and fat mass
We present a new statistical modelling approach where the response is a
function of high frequency count data. Our application is about investigating
the relationship between the health outcome fat mass and physical activity (PA)
measured by accelerometer. The accelerometer quantifies the intensity of
physical activity as counts per epoch over a given period of time. We use data
from the Avon longitudinal study of parents and children (ALSPAC) where
accelerometer data is available as a time series of accelerometer counts per
minute over seven days for a subset of children. In order to compare
accelerometer profiles between individuals and to reduce the high dimension a
functional summary of the profiles is used. We use the histogram as a
functional summary due to its simplicity, suitability and ease of
interpretation. Our model is an extension of generalised regression of scalars
on functions or signal regression. It allows also multi-dimensional functional
predictors and additive non-linear predictors for metric covariates. The
additive multidimensional functional predictors allow investigating specific
questions about whether the effect of PA varies over its intensity, by gender,
by time of day or by day of the week. The key feature of the model is that it
utilises the full profile of measured PA without requiring cut-points defining
intensity levels for light, moderate and vigorous activity. We show that the
(not necessarily causal) effect of PA is not linear and not constant over the
activity intensity. Also, there is little evidence to suggest that the effect
of PA intensity varies by gender or whether it happens on weekdays or on
weekends
Embracing open innovation to acquire external ideas and technologies and to transfer internal ideas and technologies outside
The objective of this dissertation is to increase understanding of how organizations can embrace open innovation in order to acquire external ideas and technologies from outside the organization, and to transfer internal ideas and technologies to outside the organization. The objective encompasses six sub-objectives, each addressed in one or more substudies. Altogether, the dissertation consists of nine substudies and a compendium summarizing the substudies.
An extensive literature review was conducted on open innovation and crowdsourcing literature (substudies 1–4). In the subsequent empirical substudies, both qualitative research methods (substudies 5–7) and quantitative research methods (substudies 8–9) were applied. The four literature review substudies provided insights on the body of knowledge on open innovation and crowdsourcing. These substudies unveiled most of the influential articles, authors, and journals of open innovation and crowdsourcing disciplines. Moreover, they identified research gaps in the current literature.
The empirical substudies offer several insightful findings. Substudy 5 shows how non-core ideas and technologies of a large firm can become valuable, especially for small firms. Intermediary platforms can find solutions to many pressing problems of large organizations by engaging renowned scientists from all over world (substudy 6). Intermediary platforms can also bring breakthrough innovations with novel mechanisms (substudy 7). Large firms are not only able to garner ideas by engaging their customers through crowdsourcing but they can also build long-lasting relations with their customers (substudies 8 and 9). Embracing open innovation brings challenges for firms too.
Firms need to change their organizational structures in order to be able to fully benefit from open innovation. When crowdsourcing is successful, it produces a very large number of new ideas. This has the consequence that firms need to allocate a significant amount of resources in order to identify the most promising ideas. In an idea contest, customarily, only one or a few best ideas are rewarded (substudy 7). Sometimes, no reward is provided for the selected idea (substudies 8 and 9). Most of the ideas that are received are not implemented in practice
Mean amplitude deviation calculated from raw acceleration data: a novel method for classifying the intensity of adolescents’ physical activity irrespective of accelerometer brand
A systematic review of intervention effects on potential mediators of children\u27s physical activity
Background : Many interventions aiming to increase children’s physical activity have been developed and implemented in a variety of settings, and these interventions have previously been reviewed; however the focus of these reviews tends to be on the intervention effects on physical activity outcomes without consideration of the reasons and pathways leading to intervention success or otherwise. To systematically review the efficacy of physical activity interventions targeting 5-12 year old children on potential mediators and, where possible, to calculate the size of the intervention effect on the potential mediator. Methods : A systematic search identified intervention studies that reported outcomes on potential mediators of physical activity among 5-12 year old children. Original research articles published between 1985 and April 2012 were reviewed. Results : Eighteen potential mediators were identified from 31 studies. Positive effects on cognitive/psychological potential mediators were reported in 15 out of 31 studies. Positive effects on social environmental potential mediators were reported in three out of seven studies, and no effects on the physical environment were reported. Although no studies were identified that performed a mediating analysis, 33 positive intervention effects were found on targeted potential mediators (with effect sizes ranging from small to large) and 73% of the time a positive effect on the physical activity outcome was reported. Conclusions : Many studies have reported null intervention effects on potential mediators of children’s physical activity; however, it is important that intervention studies statistically examine the mediating effects of interventions so the most effective strategies can be implemented in future programs
Puttin' on the Ritz: Pre-Ipo Enlistment of Prestigious Affiliates as Deadline-Induced Remediation
Accelerometry analysis of physical activity and sedentary behavior in older adults: a systematic review and data analysis
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