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1.
Abstract

Accelerometry is increasingly used as a physical activity surveillance device that can quantify the amount of time spent moving at a range of intensities. This study proposes physical activity intensity cut-points for the Actical accelerometer. Thirty-eight volunteers completed a multi-stage treadmill protocol at 3, 5, and 8 km · h?1 (2, 3.3, and 8 METs) while wearing Actical accelerometers initialized to collect data in 60-s epochs. Using a decision boundary analytical approach, moderate and vigorous physical activity intensity cut-points were derived for the Actical accelerometer. In adults (n = 26), the cut-point for moderate physical activity intensity occurred at 1535 counts per minute and the vigorous cut-point occurred at 3960 counts per minute. In children (n = 12), the cut-point for moderate physical activity intensity occurred at 1600 counts per minute and the vigorous cut-point occurred at 4760 counts per minute. Improved classification of physical activity intensity using the decision boundary cut-points was observed compared with using mean values for each protocol stage. The cut-points derived are recommended for use in adults. The cut-points derived for children confirm the findings of previous studies.  相似文献   

2.
Accelerometry is increasingly used as a physical activity surveillance device that can quantify the amount of time spent moving at a range of intensities. This study proposes physical activity intensity cut-points for the Actical accelerometer. Thirty-eight volunteers completed a multi-stage treadmill protocol at 3, 5, and 8 km · h?1 (2, 3.3, and 8 METs) while wearing Actical accelerometers initialized to collect data in 60-s epochs. Using a decision boundary analytical approach, moderate and vigorous physical activity intensity cut-points were derived for the Actical accelerometer. In adults (n = 26), the cut-point for moderate physical activity intensity occurred at 1535 counts per minute and the vigorous cut-point occurred at 3960 counts per minute. In children (n = 12), the cut-point for moderate physical activity intensity occurred at 1600 counts per minute and the vigorous cut-point occurred at 4760 counts per minute. Improved classification of physical activity intensity using the decision boundary cut-points was observed compared with using mean values for each protocol stage. The cut-points derived are recommended for use in adults. The cut-points derived for children confirm the findings of previous studies.  相似文献   

3.
This study examined the validity of current Actical activity energy expenditure (AEE) equations and intensity cut-points in preschoolers using AEE and direct observation as criterion measures. Forty 4–6-year-olds (5.3 ± 1.0 years) completed a ~150-min room calorimeter protocol involving age-appropriate sedentary behaviours (SBs), light intensity physical activities (LPAs) and moderate-to-vigorous intensity physical activities (MVPAs). AEE and/or physical activity intensity were calculated using Actical equations and cut-points by Adolph, Evenson, Pfeiffer and Puyau. Predictive validity was examined using paired sample t-tests. Classification accuracy was evaluated using weighted kappas, sensitivity, specificity and area under the receiver operating characteristic curve. The Pfeiffer equation significantly overestimated AEE during SB and underestimated AEE during LPA (P < 0.0125 for both). There was no significant difference between measured and predicted AEEs during MVPA. The Adolph cut-point showed significantly higher accuracy for classifying SB, LPA and MVPA than all others. The available Actical equation does not provide accurate estimates of AEE across all intensities in preschoolers. However, the Pfeiffer equation performed reasonably well for MVPA. Using cut-points of ≤6 counts · 15 s?1, 7–286 counts · 15 s?1 and ≥ 287 counts · 15 s?1 when classifying SB, LPA and MVPA, respectively, is recommended.  相似文献   

4.
The responsiveness to change of the Actical and ActiGraph accelerometers was assessed in children and adolescents. Participants (N = 208) aged 6 to 16 years completed two simulated free-living protocols, one with primarily light-to-moderate physical activity (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 5-sec, 15-sec, and 30-sec epochs. Accelerometer responsiveness for detecting differences in PA between protocols was assessed using standardized response means (SRMs). SRM values ≥.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.  相似文献   

5.
We propose and evaluate the utility of an alternative method (decision boundaries) for establishing physical activity intensity-related accelerometer cutpoints. Accelerometer data collected from seventy-six 11- to 14-year-old boys during controlled bouts of moderate- and vigorous-intensity field physical activities were assessed. Mean values and standard deviations for moderate- and vigorous-intensity activities were obtained and normal equivalents generated. The decision boundary (the point of intersection of overlapping distributions) was used to create a lower-bound vigorous-intensity cutpoint. Receiver operating characteristic (ROC) curves compared the sensitivity and specificity of the new cutpoint and mean values with the actual activity. There was a 96.5% probability that participants performing vigorous-intensity physical activity were accurately classified when using the decision boundary of 6700 counts per minute, in contrast to the 50% accurately classified when the mean value was used. Inspection of the empirical ROC curve indicated that the decision boundary provided the optimal threshold to distinguish between moderate and vigorous physical activity for this dataset. In conclusion, decision boundaries reduced the error associated with determining accelerometer threshold values. Applying these methods to accelerometer data collected in specific populations will improve the precision with which accelerometer thresholds can be identified.  相似文献   

6.
Abstract

We propose and evaluate the utility of an alternative method (decision boundaries) for establishing physical activity intensity-related accelerometer cutpoints. Accelerometer data collected from seventy-six 11- to 14-year-old boys during controlled bouts of moderate- and vigorous-intensity field physical activities were assessed. Mean values and standard deviations for moderate- and vigorous-intensity activities were obtained and normal equivalents generated. The decision boundary (the point of intersection of overlapping distributions) was used to create a lower-bound vigorous-intensity cutpoint. Receiver operating characteristic (ROC) curves compared the sensitivity and specificity of the new cutpoint and mean values with the actual activity. There was a 96.5% probability that participants performing vigorous-intensity physical activity were accurately classified when using the decision boundary of 6700 counts per minute, in contrast to the 50% accurately classified when the mean value was used. Inspection of the empirical ROC curve indicated that the decision boundary provided the optimal threshold to distinguish between moderate and vigorous physical activity for this dataset. In conclusion, decision boundaries reduced the error associated with determining accelerometer threshold values. Applying these methods to accelerometer data collected in specific populations will improve the precision with which accelerometer thresholds can be identified.  相似文献   

7.
Purpose: Ankle accelerometry allows for 24-hr data collection and improves data volume/integrity versus hip accelerometry. Using Actical ankle accelerometry, the purpose of this study was to (a) develop sensitive/specific thresholds, (b) examine validity/reliability, (c) compare new thresholds with those of the manufacturer, and (d) examine feasibility in a community sample (low-income, urban adolescent girls). Method: Two studies were conducted with 6th- through 7th-grade girls (aged 10–14 years old): First was a laboratory study (n = 24), in which 2 Actical accelerometers were placed on the ankle and worn while measuring energy expenditure (Cosmed K4b2, metabolic equivalents [METs]) during 10 prescribed activities. Analyses included device equivalence reliability (intraclass correlation coefficient [ICC], activity counts of 2 Acticals), criterion-related validity (correlation, activity counts and METs), and calculations of sensitivity, specificity, kappa, and receiver-operating characteristic curves for thresholds. The second was a free-living study (n = 459), in which an Actical was worn for more than 7 days on the ankle (full 24-hr days retained). Analyses included feasibility (frequencies, missing data) and paired t tests (new thresholds vs. those of the manufacturer). Results: In the laboratory study, the Actical demonstrated reliability (ICC = .92) and validity (r = .81). Thresholds demonstrated sensitivity (91%), specificity (84%), kappa = .73 (p = .043), area under curve range = .81–.97. In the free-living study, 99.6% of participants wore the accelerometer; 84.1% had complete/valid data (mean = 5.7 days). Primary reasons for missing/invalid data included: improper programming/documentation (5.2%), failure to return device (5.0%), and wear-time ≤ 2 days (2.8%). The moderate-to-vigorous physical activity threshold (> 3,200 counts/minute) yielded 37.2 min/day, 2 to 4.5 times lower than that of the manufacturer's software (effect size = 0.74–4.05). Conclusions: Validity, reliability, and feasibility evidences support Actical ankle accelerometry to assess physical activity in community studies of adolescent girls. When comparing manufacturers' software versus new thresholds, a major difference was observed.  相似文献   

8.
This study aimed at translating the physical activity (PA) guideline (180 min of total PA per day) into a step count target in preschoolers. 535 Flemish preschoolers (mean age: 4.41 ± 0.58) wore an ActiGraph accelerometer (GT1M, GT3X and GT3X+) – with activated step count function – for four consecutive days. The step count target was calculated from the accelerometer output using a regression equation, applying four different cut-points for light-to-vigorous PA: Pate, Evenson, Reilly, and Van Cauwenberghe. The present analysis showed that 180 min of total PA per day is equivalent to the following step count targets: 5,274 steps/day using the Pate cut-point, 4,653 steps/day using the Evenson cut-point, 11,379 steps/day using the Reilly cut-point and 13,326 steps/day using the Van Cauwenberghe cut-point. Future studies should focus on achieving consensus on which cut-points to use in preschoolers before a definite step count target in preschoolers can be proposed. Until then, we propose to use a provisional step count target of 11,500 steps/day as this step count target is attainable, realistic and helpful in promoting preschoolers’ PA.  相似文献   

9.
ABSTRACT

This study validated sedentary behaviour (SB), moderate-to-vigorous physical activity (MVPA) and vigorous physical activity (VPA) accelerometer cut-points in 5–7-year-old children. Participants (n = 49, 55% girls) wore an ActiGraph GT9X accelerometer, recording data at 100 Hz downloaded in 1 s epochs, on both wrists and the right hip during a standardised protocol and recess. Cut-points were generated using ROC analysis with direct observation as a criterion. Subsequently, cut-points were optimised using Confidence intervals equivalency analysis and then cross-validated in a cross-validation group. SB cut-points were 36 mg (Sensitivity (Sn) = 79.8%, Specificity (Sp) = 56.8%) for non-dominant wrist, 39 mg (Sn = 75.4%, Sp = 70.2%) for dominant wrist and 20 mg (Sn = 78%, Sp = 50.1%) for hip. MVPA cut-points were 189 mg (Sn = 82.6%, Sp = 78%) for non-dominant wrist, 181 mg (Sn = 79.1%, Sp = 76%) for dominant wrist and 95 mg (Sn = 79.3%, Sp = 75.6%) for hip. VPA cut-points were 536 mg (Sn = 75.1%, Sp = 68.7%) for non-dominant wrist, 534 mg (Sn = 67.6%, Sp = 95.6%) for dominant wrist and 325 mg (Sn = 78.2%, Sp = 96.1%) for hip. All placements demonstrated adequate levels of accuracy for SB and PA assessment.  相似文献   

10.
This study examined the validity of the Actical accelerometer step count and energy expenditure (EE) functions in healthy young adults. Forty-three participants participated in study 1. Actical step counts were compared to actual steps taken during a 200 m walk around an indoor track at self-selected pace and during treadmill walking at different speeds (0.894, 1.56 and 2.01 m · s–1) for 5 min. The Actical was also compared to three pedometers. For study 2, 15 participants from study 1 walked on a treadmill at their predetermined self-selected pace for 15 min. Actical EE was compared to EE measured by indirect calorimetry. One-way analysis of variance and t-tests were used to examine differences. There were no statistical difference between Actical steps and actual steps in self-selected pace walking and during treadmill walking at moderate and fast speeds. During treadmill walking at slow speed, the Actical step counts significantly under predicted actual steps taken. For study 2, there was no statistical difference between measured EE and Actical-recorded EE. The Actical provides valid estimates of step counts at self-selected pace and walking at constant speeds of 1.56 and 2.01 m · s–1. The Actical underestimates EE of walking at constants speeds ≥1.38 m · s–1.  相似文献   

11.
Accelerometry is the gold standard for field-based physical activity assessment in children; however, the plethora of devices, data reduction procedures, and cut-points available limits comparability between studies. This study aimed to compare physical activity variables from the ActiGraph GT3X+ and Actical accelerometers in children under free-living conditions. A cross-sectional study of 379 children aged 9–11 years from Ottawa (Canada) was conducted. Children wore the ActiGraph GT3X+ and Actical accelerometers on the hip simultaneously for 7 consecutive days (24-h protocol). Moderate-to-vigorous (MVPA), vigorous (VPA), moderate (MPA), and light (LPA) physical activity, as well as sedentary time, (SED) were derived using established data reduction protocols. Excellent agreement between devices was observed for MVPA (ICC = 0.73–0.80), with fair to good agreement for MPA, LPA and SED, and poor agreement for VPA. Bland-Altman plots showed excellent agreement for MVPA, LPA, and SED, adequate agreement for MPA, and poor agreement for VPA. MVPA derived from the Actical was 11.7% lower than the ActiGraph GT3X+. The ActiGraph GT3X+ and Actical are comparable for measuring children’s MVPA. However, comparison between devices for VPA, MPA, LPA, and SED are highly dependent on data reduction procedures and cut-points, and should be interpreted with caution.  相似文献   

12.
This study examined the effects of utilizing a wearable activity tracker in a credit-based physical activity instructional program (PAIP) for promoting physical activity (PA) in college students. Fourteen PAIP courses in a large public university were randomly assigned into intervention (k = 7; n = 101) and control (k = 7; n = 86) groups. All courses focused on a core curriculum that covers basic exercise and behavioral science contents through lectures and activity sessions. A Misfit Flash activity tracker was provided to students in the intervention group. Objective PA assessments occurred at baseline, mid-, and end-of-semester during a 15-week academic semester. The control group showed a significant reduction in moderate- and vigorous-intensity PA (MVPA) minutes from baseline to the end-of-semester (P <.05), whereas the intervention group showed no changes in MVPA minutes over time. However, the intervention group also showed increased sedentary time and decreased time spent in light-intensity PA during the intervention period. Taken together, the present study found null effects of utilizing the wearable activity tracker in promoting PA in college students suggesting that intervention of primary using the wearable activity tracker as a behavior change strategy may not be effective to increase in PA in this setting.  相似文献   

13.
Calibration of two objective measures of physical activity for children   总被引：9，自引：7，他引：2  
A calibration study was conducted to determine the threshold counts for two commonly used accelerometers, the ActiGraph and the Actical, to classify activities by intensity in children 5 to 8 years of age. Thirty-three children wore both accelerometers and a COSMED portable metabolic system during 15 min of rest and then performed up to nine different activities for 7 min each, on two separate days in the laboratory. Oxygen consumption was measured on a breath-by-breath basis, and accelerometer data were collected in 15-s epochs. Using receiver operating characteristic curve (ROC) analysis, cutpoints that maximised both sensitivity and specificity were determined for sedentary, moderate and vigorous activities. For both accelerometers, discrimination of sedentary behaviour was almost perfect, with the area under the ROC curve at or exceeding 0.98. For both the ActiGraph and Actical, the discrimination of moderate (0.85 and 0.86, respectively) and vigorous activity (0.83 and 0.86, respectively) was acceptable, but not as precise as for sedentary behaviour. This calibration study, using indirect calorimetry, suggests that the two accelerometers can be used to distinguish differing levels of physical activity intensity as well as inactivity among children 5 to 8 years of age.  相似文献   

14.
This study examined the metabolic cost (METs) of performing object projection skills at three practice trial intervals (6, 12, and 30 seconds). Forty adults (female n = 20) aged 18–30 (M = 23.7 ± 2.9 years) completed three, nine-minute sessions of skill trials performed at 6, 12, and 30 second intervals. Participants performed kicking, throwing and striking trials in a blocked schedule with maximal effort. Average METs during each session were measured using a COSMED K4b2. A three (interval condition) X two (sex) ANOVA was conducted to examine differences in METs across interval conditions and by sex. Results indicated a main effect for interval condition (F(5,114) = 187.02, p < .001, η2 = 0.76) with decreased interval times yielding significantly higher METs [30 sec = 3.45, 12 sec = 5.68, 6 sec = 8.21]. A main effect for sex (F(5, 114) = 35.39, p < .001, η2 = 0.24) also was found with men demonstrating higher METs across all intervals. At a rate of only two trials/min, participants elicited moderate physical activity, with 12 and 6-second intervals exhibiting vigorous PA. Demonstrating MVPA during the performance of object projection skill performance has potential implications for PA interventions.  相似文献   

15.
The purpose of this study was to validate a wireless network of accelerometers and compare it to a hip-mounted accelerometer for predicting energy expenditure in a semi-structured environment. Adults (n = 25) aged 18–30 engaged in 14 sedentary, ambulatory, exercise, and lifestyle activities over a 60-min protocol while wearing a portable metabolic analyser, hip-mounted accelerometer, and wireless network of three accelerometers worn on the right wrist, thigh, and ankle. Participants chose the order and duration of activities. Artificial neural networks were created separately for the wireless network and hip accelerometer for energy expenditure prediction. The wireless network had higher correlations (r = 0.79 vs. r = 0.72, P < 0.01) but similar root mean square error (2.16 vs. 2.09 METs, P > 0.05) to the hip accelerometer. Measured (from metabolic analyser) and predicted energy expenditure from the hip accelerometer were significantly different for the 3 of the 14 activities (lying down, sweeping, and cycle fast); conversely, measured and predicted energy expenditure from the wireless network were not significantly different for any activity. In conclusion, the wireless network yielded a small improvement over the hip accelerometer, providing evidence that the wireless network can produce accurate estimates of energy expenditure in adults participating in a range of activities.  相似文献   

16.
The present study examined the sex-specific associations of moderate and vigorous physical activity (VPA) with physical fitness in 300 Japanese adolescents aged 12–14 years. Participants were asked to wear an accelerometer to evaluate physical activity (PA) levels of various intensities (i.e. moderate PA (MPA), 3–5.9 metabolic equivalents (METs); VPA, ≥6 METs; moderate to vigorous PA (MVPA), ≥3 METs). Eight fitness items were assessed (grip strength, bent-leg sit-up, sit-and-reach, side step, 50?m sprint, standing long jump, handball throw, and distance running) as part of the Japanese standardised fitness test. A fitness composite score was calculated using Japanese fitness norms, and participants were categorised according to their score from category A (most fit) to category E (least fit), with participants in categories D and E defined as having low fitness. It was found that for boys, accumulating more than 80.7?min/day of MVPA may reduce the probability of low fitness (odds ratio (ORs) [95% confidence interval (CI)]?=?0.17 [0.06–0.47], p?=?.001). For girls, accumulating only 8.4?min of VPA could reduce the likelihood of exhibiting low fitness (ORs [95% CI]?=?0.23 [0.05–0.89], p?=?.032). These results reveal that there are sex-specific differences in the relationship between PA and physical fitness in adolescents, suggesting that sex-specific PA recommendation may be needed to improve physical fitness in adolescents.  相似文献   

17.
This study developed and validated a vector magnitude (VM) two-regression model (2RM) for use with an ankle-worn ActiGraph accelerometer. For model development, 181 youth (mean ± SD; age, 12.0 ± 1.5 yr) completed 30 min of supine rest and 2–7 structured activities. For cross-validation, 42 youth (age, 12.6 ± 0.8 yr) completed approximately 2 hr of unstructured physical activity (PA). PA data were collected using an ActiGraph accelerometer, (non-dominant ankle) and the VM was expressed as counts/5-s. Measured energy expenditure (Cosmed K4b2) was converted to youth METs (METy; activity VO2 divided by resting VO2). A coefficient of variation (CV) was calculated for each activity to distinguish continuous walking/running from intermittent activity. The ankle VM sedentary behavior threshold was ≤10 counts/5-s, and a CV≤15 counts/5-s was used to identify walking/running. The ankle VM2RM was within 0.42 METy of measured METy during the unstructured PA (P > 0.05). The ankle VM2RM was within 5.7 min of measured time spent in sedentary, LPA, MPA, and VPA (P > 0.05). Compared to the K4b2, the ankle VM2RM provided similar estimates to measured values during unstructured play and provides a feasible wear location for future studies.  相似文献   

18.
This study examined differences in physical activity (PA) estimates provided from raw and counts processing methods. One hundred and sixty-five children (87 girls) wore a hip-mounted ActiGraph GT3X+ accelerometer for 7 days. Data were available for 129 participants. Time in moderate PA (MPA), vigorous PA (VPA) and moderate-vigorous PA (MVPA) were calculated using R-package GGIR and ActiLife. Participants meeting the wear time criteria for both processing methods were included in the analysis. Time spent in MPA (?21.4 min.d?1, 95%CI ?21 to ?20) and VPA (?36 min.d?1, 95%CI ?40 to ?33) from count data were higher (P < 0.001) than raw data. Time spent in MVPA between the two processing methods revealed significant differences (All P < 0.001). Bland-Altman plots suggest that the mean bias for time spent in MPA, VPA and MVPA were large when comparing raw and count methods. Equivalence tests showed that estimates from raw and count processing methods across all activity intensities lacked equivalence. Lack of equivalence and poor agreement between raw and count processing methods suggest the two approaches to estimate PA are not comparable. Further work to facilitate the comparison of findings between studies that process and report raw and count physical activity data may be necessary.  相似文献   

19.
ABSTRACT

Purpose: The purpose of this study was to examine whether structured physical activity (PA) in a family-based community exercise program affects PA of young children and parents. Method: Twenty-two children (mean ± SD; age, 4.9 ± 2.1 years) and their parents (age, 34.3 ± 7.6 years) participated in unstructured PA sessions followed by either short- or long-duration structured PA sessions, while wearing an ActiGraph GT9X activity monitor on their right hip to estimate PA. Independent t-tests compared children’s and parents’ PA during short- and long-structured PA sessions. Paired t-tests compared short- versus long-structured PA sessions. A mixed model ANOVA compared PA during unstructured versus structured sessions and between children and parents. Results: Children spent proportionately more time in moderate-to-vigorous PA (MVPA) and had higher accelerometer counts/min than parents during short-structured PA (children:60.9 ± 18.8% vs. parents:17.7 ± 6.8%, children:3870 ± 742 vs. parents:1836 ± 556 counts/min, p < .05) and long-structured PA (children:61.1 ± 20.1% vs. parents:12.6 ± 4.9%, children:3415 ± 758 vs. parents:1604 ± 633 counts/min, p < .05). No statistical differences were found between short- and long-structured PA sessions for proportion of time spent in MVPA or counts/min for children or parents (all, p > .05). Children spent proportionally more time in MVPA and had higher counts/min during unstructured PA compared to structured PA (unstructured MVPA:54.4 ± 3.9% vs. structured MVPA:38.2 ± 4.2%, unstructured counts/min:3830 ± 222 vs. structured counts/min:2768 ± 239 counts/min; p < .05). Conclusions: Children were more active than parents during both the unstructured and structured PA sessions. However, unstructured PA sessions resulted in 63–77% and 10–11% of PA recommendations for children and adults, respectively. Family-based exercise programming can provide an opportunity for children and their parents to attain MVPA during the week.  相似文献   

20.
This study establishes tri-axial activity count (AC) cut-points for the GT3X+ accelerometer to classify physical activity intensity in overweight and obese adults. Further, we examined the accuracy of established and novel energy expenditure (EE) prediction equations based on AC and other metrics. Part 1: Twenty overweight or obese adults completed a 30 minute incremental treadmill walking protocol. Heart rate (HR), EE, and AC were measured using the GT3X+ accelerometer. Part 2: Ten overweight and obese adults conducted a self-paced external walk during which EE, AC, and HR were measured. Established equations (Freedson et al., 1998; Sasaki et al., 2011) overestimated EE by 40% and 31%, respectively (p < .01). Novel gender-specific prediction equations provided good estimates of EE during treadmill and outdoor walking (standard error of the estimate = .91 and .65, respectively). We propose new cut-points and prediction equations to estimate EE using the GT3X+ tri-axial accelerometer in overweight and obese adults.  相似文献   

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