172 resultados para accelerometry


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With the projected increase in older adults, the older driver population is estimated to be the fastest growing cohort of drivers among many developed countries. The increased physical fragility associated with the aging process make older adults who drive private automobiles a vulnerable road user group. Much of the current research on older drivers’ behaviours and practices rely on self-report data. This paper explores the utility of in-vehicle devices (Global Positioning Systems and recording accelerometers) in assessing older drivers’ habitual driving behaviours. Seventy-eight older drivers (above 65 years of age), from the Australian Capital Territory, Australia, participated in the current study. The driving behaviours and practices of these participants were prospectively assessed over a two-week period. The use of combined GPS and recording accelerometers to improve understanding of older drivers’ driving behaviours show promise within the current study. The challenges of using multiple in-vehicle devices in assessing driving beahaviours and performances within this cohort will be discussed. Based on the current findings, recommendations for future research regarding the use of in-vehicle devices among the older driver cohort are proposed.

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Purpose To evaluate the validity of a uniaxial accelerometer (MTI Actigraph) for measuring physical activity in people with acquired brain injury (ABI) using portable indirect calorimetry (Cosmed K4b(2)) as a criterion measure. Methods Fourteen people with ABI and related gait pattern impairment (age 32 +/- 8 yr) wore an MTI Actigraph that measured activity (counts(.)min-(1)) and a Cosmed K4b(2) that measured oxygen consumption (mL(.)kg(-1.)min(-1)) during four activities: quiet sitting (QS) and comfortable paced (CP), brisk paced (BP), and fast paced (FP) walking. MET levels were predicted from Actigraph counts using a published equation and compared with Cosmed measures. Predicted METs for each of the 56 activity bouts (14 participants X 4 bouts) were classified (light, moderate, vigorous, or very vigorous intensity) and compared with Cosmed-based classifications. Results Repeated-measures ANOVA indicated that walking condition intensities were significantly different (P < 0.05) and the Actigraph detected the differences. Overall correlation between measured and predicted METs was positive, moderate, and significant (r = 0.74). Mean predicted METs were not significantly different from measured for CP and BP, but for FP walking, predicted METs were significantly less than measured (P < 0.05). The Actigraph correctly classified intensity for 76.8% of all activity bouts and 91.5% of light- and moderate-intensity bouts. Conclusions Actigraph counts provide a valid index of activity across the intensities investigated in this study. For light to moderate activity, Actigraph-based estimates of METs are acceptable for group-level analysis and are a valid means of classifying activity intensity. The Actigraph significantly underestimated higher intensity activity, although, in practice, this limitation will have minimal impact on activity measurement of most community-dwelling people with ABI.

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To evaluate the validity of the ActiGraph accelerometer for the measurement of physical activity intensity in children and adolescents with cerebral palsy (CP) using oxygen uptake (VO 2) as the criterion measure. Thirty children and adolescents with CP (mean age 12.6 ± 2.0 years) wore an ActiGraph 7164 and a Cosmed K4b 2 portable indirect calorimeter during four activities; quiet sitting, comfortable paced walking, brisk paced walking and fast paced walking. VO 2 was converted to METs and activity energy expenditure and classiWed as sedentary, light or moderate-to-vigorous intensity according to the conventions for children. Mean ActiGraph counts min -1 were classiWed as sedentary, light or moderate-to-vigorous (MVPA) intensity using four diVerent sets of cut-points. VO 2 and counts min¡1 increased signiWcantly with increases in walking speed (P < 0.001). Receiver operating characteristic (ROC) curve analysis indicated that, of the four sets of cut-points evaluated, the Evenson et al. (J Sports Sci 26(14):1557-1565, 2008) cut-points had the highest classiWcation accuracy for sedentary (92%) and MVPA (91%), as well as the second highest classiWcation accuracy for light intensity physical activity (67%). A ROC curve analysis of data from our participants yielded a CP-speciWc cut-point for MVPA that was lower than the Evenson cut-point (2,012 vs. 2,296 counts min¡1), however, the diVerence in classiWcation accuracy was not statistically signiWcant 94% (95% CI = 88.2-97.7%) vs. 91% (95% CI = 83.5-96.5%). In conclusion, among children and adolescents with CP, the ActiGraph is able to diVerentiate between diVerent intensities of walking. The use of the Evenson cut-points will permit the estimation of time spent in MVPA and allows comparisons to be made between activity measured in typically developing adolescents and adolescents with CP. © 2011 Springer-Verlag.

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Background Promoting participation physical activity (PA) is an important means of promoting healthy growth and development in children with cerebral palsy (CP). The ActiGraph is a uniaxial accelerometer that provides a realtime measure of PA intensity, duration and frequency. Its small, light weight design makes it a promising measure of activity in children with CP. To date no study has validated the use of accelerometry as a measure of PA in ambulant adolescents with CP. Objectives To evaluate the validity of the ActiGraph accelerometer for measuring PA intensity in adolescents with CP, using oxygen consumption (VO2), measured using portable indirect calorimetry (Cosmed K4b2), as the criterion measure. Design Validation Study Participants/Setting: Ambulant adolescents with CP aged 10–16 years, GMFCS rating of I-III. The recruitment target is 30 (10 in each GMFCS level). Materials/Methods Participants wore the ActiGraph (counts/min) and a Cosmed K4b2 indirect calorimeter (mL/kg/min) during six activity trials: quiet sitting (QS), comfortable paced walking (CPW), brisk paced walking (BPW), fast paced walking (FPW), a ball-kicking protocol (KP) and a ball-throwing protocol (TP). MET levels (multiples of resting metabolism) for each activity were predicted from ActiGraph counts using the Freedson age-specific equation (Freedson et al. 2005) and compared with actual MET levels measured by the Cosmed. Predicted and measured METs for each activity trial were classified as light (> 1.5 METs and <4.6 METs) or moderate to vigorous intensity (≥ 4.6 METs). Results To date 36 bouts of activity have been completed (6 participants x 6 activities). Mean VO2 increased linearly as the intensity of the walking activity increased (CPW=9.47±2.16, BPW=14.06±4.38, FPW=19.21±5.68 ml/kg/min) and ActiGraph counts reflected this pattern (CPW=1099±574, BPW=2233±797 FPW=4707±1013 counts/min). The throwing protocol recording the lowest VO2 (TP=7.50±3.86 ml/kg/min) and lowest overall counts/min (TP=31±27 counts/min). When each of the 36 bouts were classified as either light or moderate to vigorous intensity using measured VO2 as the criterion measure, the Freedson equation correctly classified 28 from 36 bouts (78%). Conclusion/Clinical Implications These preliminary findings suggest that there is a relationship between the intensity of PA and direct measure of oxygen consumption and that therefore the ActiGraph may be a promising tool for accurately measuring free living PA in the community. Further data collection of the complete sample will enable secondary analysis of the relationship between PA and severity of CP (GMFCS level).

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The responsiveness to change of the Actical and ActiGraph accelerometers was assessed in children and adolescents. Participants (n=208) aged 6-16 years completed two simulated free-living protocols, one with primarily light-to-moderate physical activities (PA) and one with mostly moderate-to-vigorous PA. Time in sedentary, light, moderate, and vigorous PA was estimated using 8 previously developed cut-points (4 for Actical and 4 for ActiGraph) and 15-s and 30-s epochs. Accelerometer responsiveness for detecting differences in PA between protocols was assessed using standardized response means (SRM). SRM values >/=0.8 represented high responsiveness to change. Both accelerometers showed high responsiveness for all PA intensities (SRMs = 1.2-4.7 for Actical and 1.1-3.3 for ActiGraph). All cut-points and epoch lengths yielded high responsiveness, and choice of cut-points and epoch length had little effect on responsiveness. Thus, both the Actical and ActiGraph can detect change in PA in a simulated free-living setting, irrespective of cut-point selection or epoch length.

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Background Physical conditions through gait and other functional task are parameters to consider for frailty detection. The aim of the present study is to measure and describe the variability of acceleration, angular velocity and trunk displacement in the ten meter Extended Timed Get-Up-and-Go test in two groups of frail and non-frail elderly people through instrumentation with the iPhone4® smartphone. Secondly, to analyze the differences and performance of the variance between the study groups (frail and non-frail). This is a cross-sectional study of 30 subjects aged over 65 years, 14 frail subjects and 16 non-frail subjects. Results The highest difference between groups in the Sit-to-Stand and Stand-to-Sit subphases was in the y axis (vertical vector). The minimum acceleration in the Stand-to-Sit phase was -2.69 (-4.17 / -0.96) m/s2 frail elderly versus -8.49 (-12.1 / -5.23) m/s2 non-frail elderly, p < 0.001. In the Gait Go and Gait Come subphases the biggest differences found between the groups were in the vertical axis: -2.45 (-2.77 /-1.89) m/s2 frail elderly versus -5.93 (-6.87 / -4.51) m/s2 non-frail elderly, p < 0.001. Finally, with regards to the turning subphase, the statistically significant differences found between the groups were greater in the data obtained from the gyroscope than from the accelerometer (the gyroscope data for the mean maximum peak value for Yaw movement angular velocity in the frail elderly was specifically 25.60°/s, compared to 112.8°/s for the non-frail elderly, p < 0.05). Conclusions The inertial sensor fitted in the iPhone4® is capable of studying and analyzing the kinematics of the different subphases of the Extended Timed Up and Go test in frail and non-frail elderly people. For the Extended Timed Up and Go test, this device allows more sensitive differentiation between population groups than the traditionally used variable, namely time.

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Background : Physical activity guidelines recommend children should engage in 60 min of moderate-to-vigorous physical activity (MVPA) a day. School recess presents an opportunity for children to be physically active during the school day. Limited research has investigated children's activity levels during recess and its contribution to physical activity recommendations. Moreover, no target for physical activity during recess has been set.

Methods : One hundred sixteen boys and 112 girls (aged 5–10 years) from 23 schools had their physical activity during recess quantified using a uniaxial accelerometer during three recess breaks on one school day. The percentage of time spent engaged in moderate, high, and very high intensity activity was calculated using existing thresholds.

Results : Boys engaged in more moderate, high, and very high intensity activity than girls. On average, boys and girls spent 32.9% and 23% of recess engaged in physical activity, respectively.

Conclusions :
Boys engaged in higher intensity activities than girls. The results suggest that recess can contribute 28 min for boys and 21.5 min for girls toward the accumulation of recommended daily physical activity. However, the physical activity intensities that children engaged in were low during recess. On average, children in this study did not achieve 50% of recess time in physical activity. Interventions for increasing the physical activity of children in the playground are warranted.

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Physical activity is an integral component of a healthy lifestyle, with relationships documented between physical activity, chronic diseases, and disease risk factors. There is increasing concern that many people are not sufficiently active to benefit their health. Consequently, there is a need to determine the prevalence of physical activity engagement, identify active and inactive segments of the population, and evaluate the effectiveness of interventions. The aim of the present study was to identify and explain a number of methodological and decision-making processes associated with accelerometry, which is the most commonly used objective measure of physical activity in child and adult research.

Specifically, this review addresses:
(a) pre-data collection decisions,
(b) data collection procedures,
(c) processing of accelerometer data, and
(d) outcome variables in relation to the research questions posed.

An appraisal of the literature is provided to help researchers and practitioners begin field-based research, with recommendations offered for best practice. In addition, issues that require further investigation are identified and discussed to inform researchers and practitioners of the surrounding debates.

Overall, the review is intended as a starting point for field-based physical activity research using accelerometers and as an introduction to key issues that should be considered and are likely to be encountered at this time.

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Purpose: This study examines what volume of accelerometry data (h·d-1) is required to reliably estimate preschool children’s physical activity and whether it is necessary to include weekday and weekend data.

Methods: Accelerometry data from 493 to 799 (depending on wear time) preschool children from the Melbourne-based Healthy Active Preschool Years study were used. The percentage of wear time each child spent in total (light–vigorous) physical activity was the main outcome. Hourly increments of daily data were analyzed. t-tests, controlling for age and clustering by center of recruitment, assessed the differences between weekday and weekend physical activity. Intraclass correlation coefficients estimated reliability for an individual day. Spearman–Brown prophecy formula estimated the number of days required to reach reliability estimates of 0.7, 0.8, and 0.9.

Results: The children spent a significantly greater percentage of time being physically active on weekend compared with weekdays regardless of the minimum number of hours included (t = 12.49–16.76, P < 0.001 for all). The number of days required to reach each of the predetermined reliability estimates increased as the number of hours of data per day decreased. For instance, 2.7–2.8 d of data were required to reach a reliability estimate of 0.7 with 10 or more hours of data per day; 3.3–3.4 d were required to meet the same reliability estimate for days with 7 h of data.

Conclusions: Future studies should ensure they include the minimum amount of data (hours per day and number of days) as identified in this study to meet at least a 0.7 reliability level and should report the level of reliability for their study. In addition to weekdays, at least one weekend day should be included in analyses to reliably estimate physical activity levels for preschool children.

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An understanding of koala activity patterns is important for measuring the behavioral response of this species to environmental change, but to date has been limited by the logistical challenges of traditional field methodologies. We addressed this knowledge gap by using tri-axial accelerometer data loggers attached to VHF radio collars to examine activity patterns of adult male and female koalas in a high-density population at Cape Otway, Victoria, Australia. Data were obtained from 27 adult koalas over two 7-d periods during the breeding season: 12 in the early-breeding season in November 2010, and 15 in the late-breeding season in January 2011. Multiple 15 minute observation blocks on each animal were used for validation of activity patterns determined from the accelerometer data loggers. Accelerometry was effective in distinguishing between inactive (sleeping, resting) and active (grooming, feeding and moving) behaviors. Koalas were more active during the early-breeding season with a higher index of movement (overall dynamic body acceleration [ODBA]) for both males and females. Koalas showed a distinct temporal pattern of behavior, with most activity occurring from mid-afternoon to early morning. Accelerometry has potential for examining fine-scale behavior of a wide range of arboreal and terrestrial species.

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Background: Physical activity and sedentary behaviour in youth have been reported to vary by sex, age, weight status and country. However, supporting data are often self-reported and/or do not encompass a wide range of ages or geographical locations. This study aimed to describe objectively-measured physical activity and sedentary time patterns in youth. Methods: The International Children's Accelerometry Database (ICAD) consists of ActiGraph accelerometer data from 20 studies in ten countries, processed using common data reduction procedures. Analyses were conducted on 27,637 participants (2.8-18.4 years) who provided at least three days of valid accelerometer data. Linear regression was used to examine associations between age, sex, weight status, country and physical activity outcomes. Results: Boys were less sedentary and more active than girls at all ages. After 5 years of age there was an average cross-sectional decrease of 4.2 % in total physical activity with each additional year of age, due mainly to lower levels of light-intensity physical activity and greater time spent sedentary. Physical activity did not differ by weight status in the youngest children, but from age seven onwards, overweight/obese participants were less active than their normal weight counterparts. Physical activity varied between samples from different countries, with a 15-20 % difference between the highest and lowest countries at age 9-10 and a 26-28 % difference at age 12-13. Conclusions: Physical activity differed between samples from different countries, but the associations between demographic characteristics and physical activity were consistently observed. Further research is needed to explore environmental and sociocultural explanations for these differences.

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Determining the foraging behaviour of free-ranging marine animals is fundamental for assessing their habitat use and how they may respond to changes in the environment. However, despite recent advances in bio-logging technology, collecting information on both at-sea movement patterns and activity budgets still remains difficult in small pelagic seabird species due to the constraints of instrument size. The short-tailed shearwater, the most abundant seabird species in Australia (ca 23 million individuals), is a highly pelagic procellariiform. Despite its ecological importance to the region, almost nothing is known about its at-sea behaviour, in particular, its foraging activity. Using a combination of GPS and tri-axial accelerometer data-loggers, the fine scale three-dimensional foraging behaviour of 10 breeding individuals from two colonies was investigated. Five at-sea behaviours were identified: (1) resting on water, (2) flapping flight, (3) gliding flight, (4) foraging (i.e., surface foraging and diving events), and (5) taking-off. There were substantial intra- and inter- individual variations in activity patterns, with individuals spending on average 45.8% (range: 17.1-70.0%) of time at sea resting on water and 18.2% (range: 2.3-49.6%) foraging. Individuals made 76.4 ± 65.3 dives (range: 8-237) per foraging trip (mean duration 9.0 ± 1.9 s), with dives also recorded during night-time. With the continued miniaturisation of recording devices, the use of combined data-loggers could provide us with further insights into the foraging behaviour of small procellariiforms, helping to better understand interactions with their prey.