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.     

Non-wear or sleep? Evaluation of five non-wear detection algorithms for raw accelerometer data

ABSTRACT

Detection of non-wear periods is an important step in accelerometer data processing. This study evaluated five non-wear detection algorithms for wrist accelerometer data and two rules for non-wear detection when non-wear and sleep algorithms are implemented in parallel. Non-wear algorithms were based on the standard deviation (SD), the high-pass filtered acceleration, or tilt angle. Rules for differentiating sleep from non-wear consisted of an override rule in which any overlap between non-wear and sleep was deemed non-wear; and a 75% rule in which non-wear periods were deemed sleep if the duration was < 75% of the sleep period. Non-wear algorithms were evaluated in 47 children who wore an ActiGraph GT3X+ accelerometer during school hours for 5 days. Rules for differentiating sleep from non-wear were evaluated in 15 adults who wore a GeneActiv Original accelerometer continuously for 24 hours. Classification accuracy for the non-wear algorithms ranged between 0.86–0.95, with the SD of the vector magnitude providing the best performance. The override rule misclassified 37.1 minutes of sleep as non-wear, while the 75% rule resulted in no misclassification. Non-wear algorithms based on the SD of the acceleration signal can effectively detect non-wear periods, while application of the 75% rule can effectively differentiate sleep from non-wear when examined concurrently.     

Concurrent validation of the Actigraph gt3x+, Polar Active accelerometer,Omron HJ-720 and Yamax Digiwalker SW-701 pedometer step counts in lab-based and free-living settings

Activity monitors are frequently used to assess activity in many settings. But as technology advances, so do the mechanisms used to estimate activity causing a continuous need to validate newly developed monitors. The purpose of this study was to examine the step count validity of the Yamax Digiwalker SW-701 pedometer (YX), Omron HJ-720 T pedometer (OP), Polar Active accelerometer (PAC) and Actigraph gt3x+ accelerometer (AG) under controlled and free-living conditions. Participants completed five stages of treadmill walking (n = 43) and a subset of these completed a 3-day free-living wear period (n = 37). Manually counted (MC) steps provided a criterion measure for treadmill walking, whereas the comparative measure during free-living was the YX. During treadmill walking, the OP was the most accurate monitor across all speeds (±1.1% of MC steps), while the PAC underestimated steps by 6.7–16.0% per stage. During free-living, the OP and AG counted 97.5% and 98.5% of YX steps, respectively. The PAC overestimated steps by 44.0%, or 5,265 steps per day. The Omron pedometer seems to provide the most reliable and valid estimate of steps taken, as it was the best performer under lab-based conditions and provided comparable results to the YX in free-living. Future studies should consider these monitors in additional populations and settings.     

Decision boundaries and receiver operating characteristic curves: new methods for determining accelerometer cutpoints

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.     

Lifesource XL-18 pedometer for measuring steps under controlled and free-living conditions

The primary aim was to examine the criterion and construct validity and test–retest reliability of the Lifesource XL-18 pedometer (A&D Medical, Toronto, ON, Canada) for measuring steps under controlled and free-living activities. The influence of body mass index, waist size and walking speed on the criterion validity of XL-18 was also explored. Forty adults (35–74 years) performed a 6-min walk test in the controlled condition, and the criterion validity of XL-18 was assessed by comparing it to steps counted manually. Thirty-five adults participated in the free-living condition and the construct validity of XL-18 was assessed by comparing it to Yamax SW-200 (YAMAX Health & Sports, Inc., San Antonio, TX, USA). During the controlled condition, XL-18 did not significantly differ from criterion (P > 0.05) and no systematic error was found using Bland–Altman analysis. The accuracy of XL-18 decreased with slower walking speed (P = 0.001). During the free-living condition, Bland–Altman analysis revealed that XL-18 overestimated daily steps by 327 ± 118 than Yamax (P = 0.004). However, the absolute percent error (APE) (6.5 ± 0.58%) was still within an acceptable range. XL-18 did not differ statistically between pant pockets. XL-18 is suitable for measuring steps in controlled and free-living conditions. However, caution may be required when interpreting the steps recorded under slower speeds and free-living conditions.     

Impact of the choice of threshold on physical activity patterns in free living conditions among adolescents measured using a uniaxial accelerometer: The HELENA study

Abstract

The aim of this study was to assess the impact of the choice of threshold on physical activity patterns measured in adolescents under free living conditions (FLC) using a uniaxial accelerometer. The study comprised 2043 adolescents (12.5–17.5 years) participating in the HELENA Study. Participants wore a uniaxial accelerometer for 7 days. The PA patterns were assessed using thresholds determined from six different studies. For each of the thresholds used, the number of adolescents fulfilling the recommendation of 60 min of moderate to vigorous PA (MVPA) per day was also calculated. A significant difference was found between thresholds regardless of the activity level: differences of 38%, 207%, 136%, and 2780% for sedentary, light, moderate, and vigorous intensity PA, respectively (P < 0.001). Time of MVPA varied between methods from 25.3 to 55.2 min · day^–1. The number of adolescents fulfilling the recommendation varied from 5.9% to 37% according to the thresholds used. The kappa coefficient for concordance in the assessment of the number of adolescents achieving the PA recommendations was generally low. The definition of the threshold for PA intensity may considerably affect the PA patterns in FLC when assessed using a uniaxial accelerometer and the number of participants fulfilling the recommendations.     

Effects of Age,Walking Speed,and Body Composition on Pedometer Accuracy in Children

The objective of this study was to investigate the effects of age group, walking speed, and body composition on the accuracy of pedometer-determined step counts in children. Eighty-five participants (43 boys, 42 girls), ages 5–7 and 9–11 years, walked on a treadmill for two-minute bouts at speeds of 42, 66, and 90 m·min^-1 while wearing a spring-levered (Yamax SW-200) and a piezoelectric (New Lifestyles NL-2000) pedometer. The number of steps taken during each bout was also recorded using a hand counter. Body mass index (BMI) was calculated from height and mass, and percentage of body fat (%BF) was determined using hand-to-foot bioelectrical impedance analysis. The tilt angle of the pedometer was assessed using a magnetic protractor. Both pedometers performed well at 66 and 90 m·min^-1, but undercounted steps by approximately 20% at 42 m·min^-1. Although age group, BMI, waist circumference, and %BF did not affect pedometer accuracy, children with large pedometer tilt angles (≥ 10°) showed significantly greater percent bias than those with small tilt angles (< 10°). We suggest that the style of waistband on the child's clothing is a more important determinant of tilt angle and thus pedometer accuracy than body composition. Our results also indicate that the NL-2000 pedometer provides similar accuracy and better precision than the SW-200 pedometer, especially in children with large tilt angles. We conclude that fastening pedometers to a firm elastic belt may improve stability and reduce undercounting in young people.     

An investigation of a novel three-dimensional activity monitor to predict free-living energy expenditure

The aim of this study was to assess the capability of the 3dNX accelerometer to predict energy expenditure in two separate, free-living cohorts. Twenty-three adolescents and 14 young adults took a single dose of doubly labelled water and wore a 3dNX activity monitor during waking hours for a 10-day period while carrying out their normal routines. Multiple linear regression with backward elimination was used to establish the strength of the associations between various indices of energy expenditure, physical activity counts, and anthropometric variables. 3dNX output accounted for 27% and 35% of the variance in the total energy expenditure of the adolescent and young adult cohort, respectively. The explained variance increased to 78%, with a standard error of estimate of 7%, when 3dNX output was combined with body composition variables. The 3dNX accelerometer can be used to predict free-living daily energy expenditure with a standard error of estimate of 1.65 MJ in adolescents and 1.52 MJ in young adults. The inclusion of anthropometric variables reduces the error to approximately 1 MJ. Although it remains to cross-validate these models in other populations, early indications suggest that the 3dNX provides a useful method of predicting energy expenditure in free-living individuals.     

Decision boundaries and receiver operating characteristic curves: New methods for determining accelerometer cutpoints

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.     

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.     

Estimating Energy Expenditure With the RT3 Triaxial Accelerometer

The RT3 is a relatively new triaxial accelerometer that has replaced the TriTrac. The aim of this study was to validate the RT3 against doubly labeled water (DLW) in a free-living, mixed weight sample of adults. Total energy expenditure (TEE) was measured over a 15-day period using DLW. Activity-related energy expenditure (AEE) was estimated by subtracting resting energy expenditure and thermic effect of feeding from TEE. The RT3 triaxial accelerometer was worn over 14 consecutive days. TEE and AEE were estimated using the RT3 proprietary equation. Thirty-six adults ages 18–56 years (56% women) with an average weight of 75.9 kg (SD = 14.8) completed all measurements. Compared to DLW, the RT3 underestimated TEE by 539 kJ (4%) and AEE by 485 kJ (15%) on average. The RT3 provided a relatively accurate assessment of free-living activity-related energy expenditure at the group level and generally underestimated total and activity-related energy expenditure compared to DLW.     

Predisposed to participate? The influence of family socio-economic background on children's sports participation and daily amount of physical activity

From a Bourdieu-inspired understanding of how personal resources (‘capitals’) enable certain practices in certain contexts, the links between families' cultural, social and economic capitals, and children's daily physical activity were investigated in 500 suburban Danish schoolchildren using questionnaire data and accelerometer measures. Family socio-economic position (SEP) was found to be positively associated with children's participation in organized sport, which could be explained by differences in family capitals. By contrast, this study found no relationship between families' SEP and the amounts of general physical activity in children. This reflected the tendencies for club-organized sport to contribute a relatively small amount to the overall amount of physical activity in children, and for children of low SEP to be equally active in other settings such as school-breaks, day care and neighbourhood playgrounds.     

Methodological considerations for researchers and practitioners using pedometers to measure physical (ambulatory) activity

Researchers and practitioners require guidelines for using electronic pedometers to objectively quantify physical activity (specifically ambulatory activity) for research and surveillance as well as clinical and program applications. Methodological considerations include choice of metric and length of monitoring frame as well as different data recording and collection procedures. A systematic review of 32 empirical studies suggests we can expect 12,000-16,000 steps/day for 8-10-year-old children (lower for girls than boys); 7,000-13,000 steps/day for relatively healthy, younger adults (lower for women than men); 6,000-8,500 steps/day for healthy older adults; and 3,500-5,500 steps/day for individuals living with disabilities and chronic illnesses. These preliminary recommendations should be modified and refined, as evidence and experience using pedometers accumulates.     

Movement pattern and parameter learning in children: effects of feedback frequency

Reduced feedback during practice has been shown to be detrimental to movement accuracy in children but not in young adults. We hypothesized that the reduced accuracy is attributable to reduced movement parameter learning but not pattern learning in children. A rapid arm movement task that required the acquisition of a motorpattern scaled to specific spatial and temporal parameters was used to investigate the effects of feedback (FB) frequency (100% vs. 62% faded) on motor learning differences between 19 school-age children and 19 young adults. Adults and children practiced the task for 200 trials under the 100% or faded FB condition on day 1 and returned on day 2 for a no-FB retention test. On the retention test, children who practiced with reduced feedback performed with greater temporal parameter errors, but not pattern error than children who received frequent feedback. Motor skill learning in children is influenced byfeedback frequency during practice that affects parameter learning but not pattern learning.     

An investigation of a novel three-dimensional activity monitor to predict free-living energy expenditure

Abstract

The aim of this study was to assess the capability of the 3dNX? accelerometer to predict energy expenditure in two separate, free-living cohorts. Twenty-three adolescents and 14 young adults took a single dose of doubly labelled water and wore a 3dNX? activity monitor during waking hours for a 10-day period while carrying out their normal routines. Multiple linear regression with backward elimination was used to establish the strength of the associations between various indices of energy expenditure, physical activity counts, and anthropometric variables. 3dNX? output accounted for 27% and 35% of the variance in the total energy expenditure of the adolescent and young adult cohort, respectively. The explained variance increased to 78%, with a standard error of estimate of 7%, when 3dNX? output was combined with body composition variables. The 3dNX? accelerometer can be used to predict free-living daily energy expenditure with a standard error of estimate of 1.65 MJ in adolescents and 1.52 MJ in young adults. The inclusion of anthropometric variables reduces the error to approximately 1 MJ. Although it remains to cross-validate these models in other populations, early indications suggest that the 3dNX? provides a useful method of predicting energy expenditure in free-living individuals.     

Accelerometry to measure physical activity in toddlers: Determination of wear time requirements for a reliable estimate of physical activity

Accelerometry is widely used to evaluate physical activity in toddlers however recommendations regarding wear time are needed to understand physical activity behaviours in this age group. This study aimed to determine the minimum wear time to reliably evaluate physical activity in toddlers. Children from the 3D Birth Cohort (n = 255, 49.8% boys, 2.1 ± 0.2 years) were asked to wear an accelerometer (GT3X+, ActiGraph) for 7 days. Physical activity was expressed in active time (min/day) and counts per minute (CPM). Single day intraclass correlation coefficients (ICCs) were calculated to assess the effect of varying minimal wear time on reliability estimates. The Spearman-Brown formula was used to determine wear time required to achieve reliability levels of 70%, 80% and 90%. For active time, a reliability of 72.1% was achieved with wearing the accelerometer for ≥ 4 days of ≥ 6 h, which comprised 85.9% of the sample. For CPM, ≥ 4 days of ≥ 6 h provided a reliability of 74.7% and comprised 85.9% of the children. Results differed slightly when girls and boys were analysed separately, but restricting analyses to children with a weekend day did not. In summary, a minimum of 4 days with ≥ 6 h of accelerometry data provides a reliable estimate of physical activity in 2-year toddlers.     

Movement Pattern and Parameter Learning in Children

Reduced feedback during practice has been shown to be detrimental to movement accuracy in children but not in young adults. We hypothesized that the reduced accuracy is attributable to reduced movement parameter learning, but not pattern learning, in children. A rapid arm movement task that required the acquisition of a motor pattern scaled to specific spatial and temporal parameters was used to investigate the effects of feedback (FB) frequency (100% vs. 62% faded) on motor learning differences between 19 school-age children and 19 young adults. Adults and children practiced the task for 200 trials under the 100% or faded FB condition on Day 1 and returned on Day 2 for a no-FB retention test. On the retention test, children who practiced with reduced feedback performed with greater temporal parameter errors, but not pattern error, than children who received frequent feedback. Motor skill learning in children is influenced by feedback frequency during practice that affects parameter learning but not pattern learning.     

Comparison of Activity Type Classification Accuracy from Accelerometers Worn on the Hip,Wrists, and Thigh in Young,Apparently Healthy Adults

The purpose of this article is to compare accuracy of activity type prediction models for accelerometers worn on the hip, wrists, and thigh. Forty-four adults performed sedentary, ambulatory, lifestyle, and exercise activities (14 total, 10 categories) for 3–10 minutes each in a 90-minute semi-structured laboratory protocol. Artificial neural networks (ANNs) were developed for four accelerometers (right hip, both wrists, and right thigh,) to predict individual activities and activity categories, with direct observation (DO) as criterion. The wrist-mounted accelerometers achieved the highest accuracy for individual activities (80.9%–81.1%) and activity categories (86.6%–86.7%); accuracy was not different between wrists. The hip-mounted accelerometer had the lowest accuracy (66.2% individual activities, 72.5% activity categories); thigh-mounted accelerometer accuracy (71.4% individual activities, 84.0% activity categories) fell between the wrist- and hip-mounted accelerometers. ANNs developed for accelerometers worn on the wrists and thigh provided high accuracy for activity type prediction and represent a potential approach to physical activity (PA) assessment.