Influence of Activity Monitor Location and Bout Duration on Free-Living Physical Activity

The purpose of this study was to evaluate the influence of the location (ankle, hip, wrist) where an activity monitor (AM) is worn and of the minimum bout duration (BD) on physical activity (PA) variables during free-living monitoring. Study 1 participants wore AMs at three locations for 1 day while wearing the Intelligent Device for Energy Expenditure and Activity (IDEEA) system. Study 2 participants wore AMs at the same locations for 3 days. Variables included time (T_MV, min/day) and AEE (AEE_MV, kcal/day) for each monitor location and BD above a moderate-vigorous (MV) intensity. T_MV and AEEMV in Study 1 were similar across AMs to IDEEA values at BD = 10 min, as was T_MV in Study 2. This suggests that ankle-, wrist- and hip-worn AMs can provide similar PA outcome values during free-living monitoring at 10-min BDs.     

Moderate and vigorous physical activity intensity cut-points for the Actical accelerometer

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.     

Evaluation of Actical equations and thresholds to predict physical activity intensity in young children

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.     

Moderate and vigorous physical activity intensity cut-points for the Actical accelerometer

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.     

Wrist-based cut-points for moderate- and vigorous-intensity physical activity for the Actical accelerometer in adults

Wrist-based accelerometers are increasingly used to assess physical activity (PA) in population-based studies; however, cut-points to translate wrist-based accelerometer counts into PA intensity categories are still needed. The purpose of this study was to determine wrist-based cut-points for moderate- and vigorous-intensity ambulatory PA in adults for the Actical accelerometer. Healthy adults (n = 24) completed a four-phase treadmill exercise protocol (1.9, 3.0, 4.0 and 5.2 mph) while wearing an Actical accelerometer on their wrist. Metabolic equivalent of task (MET) levels were assessed by indirect calorimetry. Receiver operating characteristics (ROC) curves were generated to determine accelerometer counts that maximised sensitivity and specificity for classification of moderate (≥3 METs) and vigorous (>6 METs) ambulatory activity. The area under the ROC curves to discriminate moderate- and vigorous-intensity ambulatory activity were 0.93 (95% confidence interval [CI]: 0.90–0.97; P < 0.001) and 0.96 (95% CI: 0.94–0.99; P < 0.001), respectively. The identified cut-point for moderate-intensity ambulatory activity was 1031 counts per minute, which had a corresponding sensitivity and specificity of 85.6% and 87.5%, respectively. The identified cut-point for vigorous intensity ambulatory activity was 3589 counts per minute, which had a corresponding sensitivity and specificity of 88.0% and 98.7%, respectively. This study established intensity-specific cut-points for wrist-based wear of the Actical accelerometer which are recommended for quantification of moderate- and vigorous-intensity ambulatory activity.     

Comparison of ActiGraph GT3X+ and Actical accelerometer data in 9–11-year-old Canadian children

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.     

Ankle Accelerometry for Assessing Physical Activity Among Adolescent Girls: Threshold Determination,Validity, Reliability,and Feasibility

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.     

The measurement of physical activity in young children

Two studies were conducted to determine the validity of various measures of physical activity in young children. In Study 1, 21 preschool children were utilized to explore how well measures of children's activity obtained from parents, teachers, and the children predicted observed behavior at school and in the home. Study 2 (n = 51 preschool children) focused on the predictive validity of the Caltrac motion sensor. In both studies, detailed minute-by-minute ratings of children's activity in Study 1 were generally ineffective in predicting observed physical activity. Children's activity preferences, however, were significantly related to the proportion of high intensity physical activity performed. In Study 2, there was a significant relationship (r = .86, p less than .0001) between Caltrac readings and observed physical activity. This correlation was similar for boys and girls, normal and overweight children, and younger and older children. These findings suggest that the Caltrac monitor may provide a valid index of individual differences in physical activity in young children.     

Wear compliance,sedentary behaviour and activity in free-living children from hip-and wrist-mounted ActiGraph GT3X+ accelerometers

The GT3X+ worn at the wrist promotes greater compliance than at the hip. Minutes in SB and PA calculated from raw accelerations at the hip and wrist provide contrasting estimates and cannot be directly compared.

Wear-time for the wrist (15.6 to 17.4 h.d^?1) was greater than the hip (15.2 to 16.8 h.d^?1) across several wear-time criteria (all P < 0.05). Moderate-strong associations were found between time spent in SB (r = 0.39), LPA (r = 0.33), MPA (r = 0.99), VPA (r = 0.82) and MVPA (r = 0.81) between the two device placements (All P < 0.001). The wrist device detected more minutes in LPA, MPA, VPA and MVPA whereas the hip detected more SB (all P = 0.001). Estimates of time in SB and all activity outcomes from the wrist and hip lacked equivalence.

One hundred and eighty-eight 9–12-year-old children wore a wrist- and hip-mounted accelerometer for 7 days. Data were available for 160 (hip) and 161 (wrist) participants. Time spent in SB and PA was calculated using GGIR.

This study examined the compliance of children wearing wrist- and hip-mounted ActiGraph GT3X+ accelerometers and compared estimates of sedentary behaviour (SB) and physical activity (PA) between devices.     

Discrepancies in accelerometer-measured physical activity in children due to cut-point non-equivalence and placement site

Abstract This study examined dissonance in physical activity (PA) between two youth-specific hip-derived intensity cut-points for the Actiwatch (AW), and compared PA between hip and wrist placements using site-specific cut-points. Twenty-four children aged 11.2?±?0.5 years wore the AW on the right hip and non-dominant wrist during a typical school day. Minutes of sedentary behaviour and vigorous activity were greater using Puyau et al. (2002) cut-points, but light, moderate, and moderate-to-vigorous (MVPA) were greater when derived using Puyau et al. (2004) cut-points (P?相似文献   

Cut points of the Actigraph GT9X for moderate and vigorous intensity physical activity at four different wear locations

ABSTRACT

Accelerometer cut points are an important consideration for distinguishing the intensity of activity into categories such as moderate and vigorous. It is well-established in the literature that these cut points depend on a variety of factors, including age group, device, and wear location. The Actigraph GT9X is a newer model accelerometer that is used for physical activity research, but existing cut points for this device are limited since it is a newer device. Furthermore, there is not existing data on cut points for the GT9X at the ankle or foot locations, which offers some potential benefit for activities that do not involve arm and/or core motion. A total of N = 44 adults completed a four-stage treadmill protocol while wearing Actigraph GT9X sensors at four different locations: foot, ankle, wrist, and hip. Metabolic Equivalent of Task (MET) levels assessed by indirect calorimetry along with Receiver Operating Characteristic (ROC) curves were used to establish cut points for moderate and vigorous intensity for each wear location of the GT9X. Area under the ROC curves indicated high discrimination accuracy for each case.     

Development of raw acceleration cut-points for wrist and hip accelerometers to assess sedentary behaviour and physical activity in 5–7-year-old children

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.     

Validation of a wireless accelerometer network for energy expenditure measurement

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.     

Validity of the Actical activity monitor for assessing steps and energy expenditure during walking

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.     

Evaluating the Responsiveness of Accelerometry to Detect Change in Physical Activity

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.     

Familial aggregation in physical activity.

The purposes of this study were to (a) examine the stability and consistency of the Caltrac accelerometer (Hemokinetics, Madison, WI) and an activity record to assess physical activity in children and adults (Experiment 1), and (b) to determine if there is a relationship between parents and their children in physical activity level (Experiment 2). Thirty 5-9-year-old children and their biological parents wore Caltrac accelerometers for three consecutive days (including one weekend day). At the same time, parents completed a Caltrac Activity Record (CAL REC) for themselves and their child. Dependent variables were counts per day for the Caltrac and minutes of light activity and activity for the CAL REC. Between-day correlations for the Caltrac ranged from r = .73 to .87 for the parents (p less than .001) and from r = .38 (p less than .04) to .79 (p less than .001) for the children. An analysis of variance with repeated measures indicated no significant differences for the Caltrac between days for parents and children. Between-day correlations for CAL REC ranged from r = .67 to .91 (p less than .05) for parents and r = .36 to .72 (p less than .05) for children, and there were no significant differences between days. In Experiment 2, chi 2 analyses were used to examine familial resemblance in physical activity. Using the Caltrac, familial resemblance occurred in 67% (father and child) and 73% (mother and child) of the families. Using the CAL REC, familial aggregation was present in 70% (father and child) and 66% (mother and child) of the families.(ABSTRACT TRUNCATED AT 250 WORDS)     

Calibration of two objective measures of physical activity for children

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.     

Kinetics and kinematics analysis of incremental cycling to exhaustion

Technique changes in cyclists are not well described during exhaustive exercise. Therefore the aim of the present study was to analyze pedaling technique during an incremental cycling test to exhaustion. Eleven cyclists performed an incremental cycling test to exhaustion. Pedal force and joint kinematics were acquired during the last three stages of the test (75%, 90% and 100% of the maximal power output). Inverse dynamics was conducted to calculate the net joint moments at the hip, knee and ankle joints. Knee joint had an increased contribution to the total net joint moments with the increase of workload (5-8% increase, p < 0.01). Total average absolute joint moment and knee joint moment increased during the test (25% and 39%, for p < 0.01, respectively). Increases in plantar flexor moment (32%, p < 0.01), knee (54%, p < 0.01) and hip flexor moments (42%, p = 0.02) were found. Higher dorsiflexion (2%, for p = 0.03) and increased range of motion (19%, for p = 0.02) were observed for the ankle joint. The hip joint had an increased flexion angle (2%, for p < 0.01) and a reduced range of motion (3%, for p = 0.04) with the increase of workload. Differences in joint kinetics and kinematics indicate that pedaling technique was affected by the combined fatigue and workload effects.     

On the generality of the "sit and reach" test: an analysis of flexibility data for an aging population

Head rotation, shoulder extension and rotation, ankle plantar and dorsiflexion, hip flexion, and sit and reach (SR) scores were examined in 41 women and 39 men, aged 45-75 years. The SR gave more reproducible data than the other measurements (intraclass test/retest correlation over 8 months, r = .83). SR scores were independent of standing height (r2 = .068) but were greater in women (p less than .002). The flexibility at all joints was less than reported for young adults. There were age-related decreases of flexibility scores for the head and shoulder joints (p less than .01), with a parallel trend (p less than .05) for ankle plantar flexion and SR scores (the last only after inclusion of an age-gender interaction term). A principal components analysis identified three factors (tentatively identified as general trunk, ankle, and shoulder flexibility) accounting for 55.9% of total variance. SR scores had a moderate correlation with the first factor (r = .61) but only weak correlations with the second and third. Although the SR test is the most reliable simple instrument, it provides only limited information about the flexibility at other joints in an older population.