Most accelerometers are worn around the waist (hip or lower back) to assess habitual physical activity. Wrist-worn accelerometers may be an alternative to the waist-worn monitors and may improve compliance in studies with prolonged wear. The aim of this study was to validate the Vivago® Wrist-Worn Accelerometer at various intensities of physical activity (PA) in adults.
Twenty-one healthy adults aged 20–34 years were recruited for the study. Accelerometer data and oxygen uptake (VO2) were measured at sedentary, light, moderate and vigorous levels of PA.
Activity categories and accelerometer counts were: sedentary, 0–15 counts·min−1; light, 16–40 counts·min−1; moderate, 41–85 counts·min−1; and vigorous activity, >; 85 counts·min−1. ANOVA repeated measures was used to determine the relationship between accelerometry data output and oxygen consumption (r = .89; p <; .001). The Bland and Altman method showed good agreement in the assessment of energy expenditure between the indirect calorimetry and the data obtained by the accelerometer.
Results of the study suggest that the Vivago® wrist-worn accelerometer is a valid measure of PA at varying levels of intensity. The study has also defined threshold values at 4 intensities and hence te Vivago® accelerometer may be used to quantify PA in free living conditions among adults. This device has possible application in treating a variety of important health concerns.
Cut-off; Accelerometry; Exercise; Validation; Calibration
The aim of this study was to compare physical activity measured using GT1M ActiGraph and GT3X ActiGraph accelerometers in free living conditions.
Twenty-five adults wore GT1M and GT3X Actigraph accelerometers simultaneously during a typical weekday of activity. Data were uploaded from the monitor to a computer at the end of test (one day). Previously established thresholds were used for defining time spent at each level of physical activity, physical activity was assessed at varying intensities comparing data from the two accelerometers by ANOVA and Bland and Altman statistical analysis. The concordance correlation coefficient between accelerometers at each intensity level was 0.99. There were no significant differences between accelerometers at any of the activity levels. Differences between data obtained in minutes with the GT1M accelerometer and the GT3X monitor were to 0.56, 0.36, 0.52 and 0.44% for sedentary, light, moderate and vigorous, respectively. The Bland and Altman method showed good agreement between data obtained for the two accelerometers.
Findings suggest that the two accelerometers provided similar results and therefore the GT3X may be used in clinical and epidemiological studies without additional calibration or validation studies.
Accelerometry; Physical activity; Assessment; Equivalence
To investigate the association between physical activity and health, we need accurate and detailed free-living physical activity measurements. The determination of energy expenditure of activity (EEACT) may also be useful in the treatment and maintenance of nutritional diseases such as diabetes mellitus. Minute-to-minute energy expenditure during a 24-h period was measured in 60 sedentary normal female volunteers (35.4 ± 9.0 years, body mass index 30.0 ± 5.9 kg/m2), using a state-of-the-art whole-room indirect calorimeter. The activities ranged from sedentary deskwork to walking and stepping at different intensities. Body movements were simultaneously measured using a hip-worn triaxial accelerometer (Tritrac-R3D, Hemokentics, Inc., Madison, Wisconsin) and a wrist-worn uniaxial accelerometer (ActiWatch AW64, MiniMitter Co., Sunriver, Oregon) on the dominant arm. Movement data from the accelerometers were used to develop nonlinear prediction models (separately and combined) to estimate EEACT and compared for accuracy. In a subgroup (n = 12), a second 24-h study period was repeated for cross-validation of the combined model. The combined model, using Tritrac-R3D and ActiWatch, accurately estimated total EEACT (97.7 ± 3.2% of the measured values, p = 0.781), as compared with using ActiWatch (86.0 ± 4.7%, p < 0.001) or Tritrac-R3D (90.0 ± 4.6%, p < 0.001) alone. This model was also accurate for all intensity categories during various physical activities. The subgroup cross-validation also showed accurate and reproducible predictions by the combination model. In this study, we demonstrated that movement measured using accelerometers at the hip and wrist could be used to accurately predict EEACT of various types and intensity of activities. This concept can be extended to develop valid models for the accurate measurement of free-living energy metabolism in clinical populations.
The purpose of this study was to determine whether the published left-wrist cut-points for the triaxial GENEA accelerometer, are accurate for predicting intensity categories during structured activity bouts.
A convenience sample of 130 adults wore a GENEA accelerometer on their left wrist while performing 14 different lifestyle activities. During each activity, oxygen consumption was continuously measured using the Oxycon mobile. Statistical analysis used Spearman's rank correlations to determine the relationship between measured and estimated intensity classifications. Cross tabulation tables were constructed to show under- or over-estimation of misclassified intensities. One-way chi-square tests were used to determine whether the intensity classification accuracy for each activity differed from 80%.
For all activities the GENEA accelerometer-based physical activity monitor explained 41.1% of the variance in energy expenditure. The intensity classification accuracy was 69.8% for sedentary activities, 44.9% for light activities, 46.2% for moderate activities, and 77.7% for vigorous activities. The GENEA correctly classified intensity for 52.9% of observations when all activities were examined; this increased to 61.5% with stationary cycling removed.
A wrist-worn triaxial accelerometer has modest intensity classification accuracy across a broad range of activities, when using the cut-points of Esliger et al. Although the sensitivity and specificity are less than those reported by Esliger et al., they are generally in the same range as those reported for waist-worn, uniaxial accelerometer cut-points.
activity monitor; accelerometry; physical activity; energy expenditure
Physical activity and sedentary behaviour among children should be measured accurately in order to investigate their relationship with health. Accelerometry provides objective and accurate measurement of body movement, which can be converted to meaningful behavioural outcomes. The aim of this study was to evaluate the best evidence for the decisions on data collection and data processing with accelerometers among children resulting in a standardized protocol for use in the participating countries.
This cross-sectional accelerometer study was conducted as part of the European ENERGY-project that aimed to produce an obesity prevention intervention among schoolchildren. Five countries, namely Belgium, Greece, Hungary, Switzerland and the Netherlands participated in the accelerometer study. We used three different Actigraph models-Actitrainers (triaxial), GT3Xs and GT1Ms. Children wore the device for six consecutive days including two weekend days. We selected an epoch length of 15 seconds. Accelerometers were placed at children's waist at the right side of the body in an elastic belt.
In total, 1082 children participated in the study (mean age = 11.7 ± 0.75 y, 51% girls). Non-wearing time was calculated as periods of more than 20 minutes of consecutive zero counts. The minimum daily wearing time was set to 10 hours for weekdays and 8 hours for weekend days. The inclusion criterion for further analysis was having at least three valid weekdays and one valid weekend day. We selected a cut-point (count per minute (cpm)) of <100 cpm for sedentary behaviour, <3000 cpm for light, <5200 cpm for moderate, and >5200 cpm for vigorous physical activity. We also created time filters for school-time during data cleaning in order to explore school-time physical activity and sedentary behaviour patterns in particular.
This paper describes the decisions for data collection and processing. Use of standardized protocols would ease future use of accelerometry and the comparability of results between studies.
The primary study aim was to evaluate associations of estimated weekly minutes of moderate-to-vigorous intensity exercise from self-reports of the telephone-administered 7-Day Physical Activity Recall (PAR) with data captured by the RT3 triaxial accelerometer.
This investigation was undertaken as part of the FRESH START study, a randomized clinical trial that tested an iteratively-tailored diet and exercise mailed print intervention among newly diagnosed breast and prostate cancer survivors. A convenience sample of 139 medically-eligible subjects living within a 60-mile radius of the study center provided both 7-Day PAR and accelerometer data at enrollment. Ultimately n=115 substudy subjects were found eligible for the FRESH START study and randomized to one of two study treatment arms. Follow-up assessments at Year 1 (n=103) and Year 2 (n=99) provided both the 7-Day PAR and accelerometer data.
There was moderate agreement between the 7-Day PAR and the accelerometer with longitudinal serial correlation coefficients of .54 (baseline), .24 (Year 1) and .53 (Year 2), all P-values < .01, though the accelerometer estimates for weekly time in moderate-to-vigorous physical activity were much higher than those of the 7-Day PAR at all time points. The two methods were poorly correlated in assessing sensitivity to change from baseline to Year 1 (rho=.11, P=.30). Using mixed models repeated measures analysis, both methods exhibited similar non-significant treatment arm X time interaction P-values (7-Day PAR=.22, accelerometer=.23).
The correlations for three serial time points were in agreement with findings of other studies that compared self-reported time in exercise with physical activity captured by accelerometry. However, these methods capture somewhat different dimensions of physical activity and provide differing estimates of change over time.
Exercise; Measurement; Self Report; Activity Monitor; Repeated Measures
To determine the reliability and validity of the Multimedia Activity Recall for Children and Adults (MARCA) in people with chronic obstructive pulmonary disease (COPD).
People with COPD and their carers completed the Multimedia Activity Recall for Children and Adults (MARCA) for four, 24-hour periods (including test-retest of 2 days) while wearing a triaxial accelerometer (Actigraph GT3X+®), a multi-sensor armband (Sensewear Pro3®) and a pedometer (New Lifestyles 1000®).
Self reported activity recalls (MARCA) and objective activity monitoring (Accelerometry) were recorded under free-living conditions.
24 couples were included in the analysis (COPD; age 74.4±7.9 yrs, FEV1 54±13% Carer; age 69.6±10.9 yrs, FEV1 99±24%).
Main Outcome Measure(s)
Test-retest reliability was compared for MARCA activity domains and different energy expenditure zones. Validity was assessed between MARCA-derived physical activity level (in metabolic equivalent of task (MET) per minute), duration of moderate to vigorous physical activity (min) and related data from the objective measurement devices. Analysis included intra-class correlation coefficients (ICC), Bland-Altman analyses, paired t-tests (p) and Spearman's rank correlation coefficients (rs).
Reliability between occasions of recall for all activity domains was uniformly high, with test-retest correlations consistently >0.9. Validity correlations were moderate to strong (rs = 0.43–0.80) across all comparisons. The MARCA yields comparable PAL estimates and slightly higher moderate to vigorous physical activity (MVPA) estimates.
In older adults with chronic illness, the MARCA is a valid and reliable tool for capturing not only the time and energy expenditure associated with physical and sedentary activities but also information on the types of activities.
This paper describes an experimental study in estimating energy expenditure from treadmill walking using a single hip-mounted triaxial inertial sensor comprised of a triaxial accelerometer and a triaxial gyroscope. Typical physical activity characterization using accelerometer generated counts suffers from two drawbacks - imprecison (due to proprietary counts) and incompleteness (due to incomplete movement description). We address these problems in the context of steady state walking by directly estimating energy expenditure with data from a hip-mounted inertial sensor. We represent the cyclic nature of walking with a Fourier transform of sensor streams and show how one can map this representation to energy expenditure (as measured by V O2 consumption, mL/min) using three regression techniques - Least Squares Regression (LSR), Bayesian Linear Regression (BLR) and Gaussian Process Regression (GPR). We perform a comparative analysis of the accuracy of sensor streams in predicting energy expenditure (measured by RMS prediction accuracy). Triaxial information is more accurate than uniaxial information. LSR based approaches are prone to outlier sensitivity and overfitting. Gyroscopic information showed equivalent if not better prediction accuracy as compared to accelerometers. Combining accelerometer and gyroscopic information provided better accuracy than using either sensor alone. We also analyze the best algorithmic approach among linear and nonlinear methods as measured by RMS prediction accuracy and run time. Nonlinear regression methods showed better prediction accuracy but required an order of magnitude of run time. This paper emphasizes the role of probabilistic techniques in conjunction with joint modeling of triaxial accelerations and rotational rates to improve energy expenditure prediction for steady-state treadmill walking.
Accelerometer; Energy expenditure; Gyroscope; Treadmill walking
To assess performance of existing wear/nonwear time classification algorithms for accelerometry data collected in the free-living environment using a wrist-worn triaxial accelerometer and a waist-worn uniaxial accelerometer in older adults.
Twenty-nine adults aged 76 to 96 years wore wrist accelerometers for ~24-h per day and waist accelerometers during waking for ~7 days of free-living. Wear and nonwear times were classified by existing algorithms (Alg[Actilife], Alg[Troiano] and Alg[Choi]) and compared with wear and nonwear times identified by data plots and diary records. Using bias and probability of correct classification, performance of the algorithms, two time-windows (60- and 90-min), and vector magnitude (VM) vs. vertical axis (V) counts from a triaxial accelerometer, were compared.
Automated algorithms (Alg[Choi] and Alg[Troiano]) classified wear/nonwear time intervals more accurately from VM than V counts. The use of 90-min time window improved wear/nonwear classification accuracy when compared with the 60-min window. The Alg[Choi] and Alg[Troiano] performed better than the manufacturer-provided algorithm (Alg[Actilife]), and Alg[Choi] performed better than Alg[Troiano] for wear/nonwear time classification using data collected by both accelerometers.
Triaxial wrist-worn accelerometer can be used for an accurate wear/nonwear time classification in free-living older adults. The use of 90-min window and VM counts improves performance of commonly used algorithms for wear/nonwear classification for both uniaxial and triaxial accelerometers.
nonwear categorization; physical activity assessment; accelerometry; sedentary behavior
The goal of this study is to develop, test, and compare multinomial logistic regression (MLR) and support vector machines (SVM) in classifying preschool-aged children physical activity data acquired from an accelerometer. In this study, 69 children aged 3–5 years old were asked to participate in a supervised protocol of physical activities while wearing a triaxial accelerometer. Accelerometer counts, steps, and position were obtained from the device. We applied K-means clustering to determine the number of natural groupings presented by the data. We used MLR and SVM to classify the six activity types. Using direct observation as the criterion method, the 10-fold cross-validation (CV) error rate was used to compare MLR and SVM classifiers, with and without sleep. Altogether, 58 classification models based on combinations of the accelerometer output variables were developed. In general, the SVM classifiers have a smaller 10-fold CV error rate than their MLR counterparts. Including sleep, a SVM classifier provided the best performance with a 10-fold CV error rate of 24.70%. Without sleep, a SVM classifier-based triaxial accelerometer counts, vector magnitude, steps, position, and 1- and 2-min lag and lead values achieved a 10-fold CV error rate of 20.16% and an overall classification error rate of 15.56%. SVM supersedes the classical classifier MLR in categorizing physical activities in preschool-aged children. Using accelerometer data, SVM can be used to correctly classify physical activities typical of preschool-aged children with an acceptable classification error rate.
Accelerometers; activity monitoring; classification; multinomial logistic regression classifiers; support vector machines classifiers
Accelerometry is an important method for extending our knowledge about intensity, duration, frequency and patterns of physical activity needed to promote health. This study has used accelerometry to detect associations between intensity levels and related activity patterns with multimorbidity and disability. Moreover, the proportion of people meeting the physical activity recommendations for older people was assessed.
Physical activity was measured in 168 subjects (78 males; 65–89 years of age), using triaxial GT3X accelerometers for ten consecutive days. The associations between physical activity parameters and multimorbidity or disability was examined using multiple logistic regression models, which were adjusted for gender, age, education, smoking, alcohol consumption, lung function, nutrition and multimorbidity or disability.
35.7% of the participants met the physical activity recommendations of at least 150 minutes of moderate to vigorous activity per week. Only 11.9% reached these 150 minutes, when only bouts of at least 10 minutes were counted. Differences in moderate to vigorous activity between people with and without multimorbidity or disability were more obvious when shorter bouts instead of only longer bouts were included. Univariate analyses showed an inverse relationship between physical activity and multimorbidity or disability for light and moderate to vigorous physical activity. A higher proportion of long activity bouts spent sedentarily was associated with higher risk for multimorbidity, whereas a high proportion of long bouts in light activity seemed to prevent disability. After adjustment for covariates, there were no significant associations, anymore.
The accumulated time in moderate to vigorous physical activity seems to have a stronger relationship with health and functioning when shorter activity bouts and not only longer bouts were counted. We could not detect an association of the intensity levels or activity patterns with multimorbidity or disability in elderly people after adjustment for covariates.
Recent interest in sedentary behavior and technological advances expanded use of watch-size accelerometers for continuous monitoring of physical activity (PA) over extended periods (e.g., 24 h/day for 1 week) in studies conducted in natural living environment. This approach necessitates the development of new methods separating bedtime rest and activity periods from the accelerometer recordings. The goal of this study was to develop a decision tree with acceptable accuracy for separating bedtime rest from activity in youth using accelerometer placed on waist or wrist. Minute-by-minute accelerometry data were collected from 81 youth (10–18 years old, 47 females) during a monitored 24-h stay in a whole-room indirect calorimeter equipped with a force platform covering the floor to detect movement. Receiver Operating Characteristic (ROC) curve analysis was used to determine the accelerometer cut points for rest and activity. To examine the classification differences, the accelerometer bedtime rest and activity classified by the algorithm in the development group (n = 41) were compared with actual bedtime rest and activity classification obtained from the room calorimeter-measured metabolic rate and movement data. The selected optimal bedtime rest cut points were 20 and 250 counts/min for the waist- and the wrist-worn accelerometer, respectively. The selected optimal activity cut points were 500 and 3,000 counts/min for waist and wrist-worn accelerometers, respectively. Bedtime rest and activity were correctly classified by the algorithm in the validation group (n = 40) by both waist- (sensitivity: 0.983, specificity: 0.946, area under ROC curve: 0. 872) and wrist-worn (0.999, 0.980 and 0.943) accelerometers. The decision tree classified bedtime rest correctly with higher accuracy than commonly used automated algorithm for both waist- and wrist-warn accelerometer (all p<0.001). We concluded that cut points developed and validated for waist- and wrist-worn uniaxial accelerometer have a good power for accurate separation of time spent in bedtime rest from activity in youth.
Lack of physical activity may be an important etiological factor in the current epidemiological transition characterised by increasing prevalence of obesity and chronic diseases in sub-Sahara Africa. However, there is a dearth of data on objectively measured physical activity energy expenditure (PAEE) in this region. We sought to develop regression equations using body composition and accelerometer counts to predict PAEE. We conducted a cross-sectional study of 33 adult volunteers from an urban (n=16) and a rural (n=17) residential site in Cameroon. Energy expenditure was measured by doubly labelled water over a period of 7 consecutive days. Simultaneously, a hip-mounted Actigraph® accelerometer recorded body movement. PAEE prediction equations were derived using accelerometer counts, age, sex and body composition variables, and cross-validated by the jack-knife method. The Bland and Altman limits of agreement (LOA) approach was used to assess agreement. Our results show that PAEE (kJ·kg−1·day−1) was significantly and positively correlated with activity counts from the accelerometer (r=0.37, p=0.03). The derived equations explained 14 to 40% of the variance in PAEE. Age, sex and accelerometer counts together explained 34% of the variance in PAEE, with accelerometer counts alone explaining 14%. The LOA between DLW and the derived equations were wide, with predicted PAEE being up to 60 kJ·kg−1·day−1 below or above the measured value. In summary, the derived equations performed better than existing published equations in predicting PAEE from accelerometer counts in this population. Accelerometry could be used to predict PAEE in this population and therefore has important applications for monitoring population levels of total physical activity patterns.
The assessment of physical activity in healthy populations and in those with chronic diseases is challenging. The aim of this systematic review was to identify whether available activity monitors (AM) have been appropriately validated for use in assessing physical activity in these groups. Following a systematic literature search we found 134 papers meeting the inclusion criteria; 40 conducted in a field setting (validation against doubly labelled water), 86 in a laboratory setting (validation against a metabolic cart, metabolic chamber) and 8 in a field and laboratory setting. Correlation coefficients between AM outcomes and energy expenditure (EE) by the criterion method (doubly labelled water and metabolic cart/chamber) and percentage mean differences between EE estimation from the monitor and EE measurement by the criterion method were extracted. Random-effects meta-analyses were performed to pool the results across studies where possible. Types of devices were compared using meta-regression analyses. Most validation studies had been performed in healthy adults (n = 118), with few carried out in patients with chronic diseases (n = 16). For total EE, correlation coefficients were statistically significantly lower in uniaxial compared to multisensor devices. For active EE, correlations were slightly but not significantly lower in uniaxial compared to triaxial and multisensor devices. Uniaxial devices tended to underestimate TEE (−12.07 (95%CI; -18.28 to −5.85) %) compared to triaxial (−6.85 (95%CI; -18.20 to 4.49) %, p = 0.37) and were statistically significantly less accurate than multisensor devices (−3.64 (95%CI; -8.97 to 1.70) %, p<0.001). TEE was underestimated during slow walking speeds in 69% of the lab validation studies compared to 37%, 30% and 37% of the studies during intermediate, fast walking speed and running, respectively. The high level of heterogeneity in the validation studies is only partly explained by the type of activity monitor and the activity monitor outcome. Triaxial and multisensor devices tend to be more valid monitors. Since activity monitors are less accurate at slow walking speeds and information about validated activity monitors in chronic disease populations is lacking, proper validation studies in these populations are needed prior to their inclusion in clinical trials.
Chronic diseases; Doubly labelled water; Indirect calorimetry; Activity monitoring; Physical activity; Validation study; Systematic review
There is general consensus that physical activity is important for preserving functional capacities of older adults and positively influencing quality of life. While accelerometry is widely accepted and applied to assess physical activity in studies, several problems with this method remain (e.g., low retest reliability, measurement errors). The aim of this study was to test the intra-instrumental retest reliability of a wrist-worn accelerometer in a 3-day measurement of physical activity in older adults and to compare different estimators. A sample of 123 older adults (76.5 ± 5.1 years, 59 % female) wore a uniaxial accelerometer continuously for 1 week. The data were split into two repeated measurement values (week set) of 3 days each. The sum, the 80–99th quantiles and the 80–99th trimmed sums were built for each week set. Retest reliability was assessed for each estimator and graphically demonstrated by Bland–Altman plots. The intraclass correlation of the retest reliability ranged from 0.22 to 0.91. Retest reliability increases when a more robust estimator than the overall sum is used. Therefore, the trimmed sum can be recommended as a conservative estimate of the physical activity level of older adults.
Aged; Reproducibility of results; Activities of daily living; Bias (epidemiology)
Metabolic functions typically increase with human activity, but optimal methods to characterize activity levels for real-time predictions of ventilation volume (l/min) during exposure assessments have not been available. Could tiny, triaxial accelerometers be incorporated into personal level monitors to define periods of acceptable wearing compliance, and allow the exposures (μg/m3) to be extended to potential doses in μg/min/kg of body weight?
In a pilot effort, we tested: 1) whether appropriately-processed accelerometer data could be utilized to predict compliance and in linear regressions to predict ventilation volumes in real time as an on-board component of personal level exposure sensor systems, and 2) whether locating the exposure monitors on the chest in the breathing zone, provided comparable accelerometric data to other locations more typically utilized (waist, thigh, wrist, etc.).
Prototype exposure monitors from RTI International and Columbia University were worn on the chest by a pilot cohort of adults while conducting an array of scripted activities (all <10 METS), spanning common recumbent, sedentary, and ambulatory activity categories. Referee Wocket accelerometers that were placed at various body locations allowed comparison with the chest-located exposure sensor accelerometers. An Oxycon Mobile mask was used to measure oral-nasal ventilation volumes in-situ. For the subset of participants with complete data (n= 22), linear regressions were constructed (processed accelerometric variable versus ventilation rate) for each participant and exposure monitor type, and Pearson correlations computed to compare across scenarios.
Triaxial accelerometer data were demonstrated to be adequately sensitive indicators for predicting exposure monitor wearing compliance. Strong linear correlations (R values from 0.77 to 0.99) were observed for all participants for both exposure sensor accelerometer variables against ventilation volume for recumbent, sedentary, and ambulatory activities with MET values ~<6. The RTI monitors mean R value of 0.91 was slightly higher than the Columbia monitors mean of 0.86 due to utilizing a 20 Hz data rate instead of a slower 1 Hz rate. A nominal mean regression slope was computed for the RTI system across participants and showed a modest RSD of +/−36.6%. Comparison of the correlation values of the exposure monitors with the Wocket accelerometers at various body locations showed statistically identical regressions for all sensors at alternate hip, ankle, upper arm, thigh, and pocket locations, but not for the Wocket accelerometer located at the dominant-side wrist location (R=0.57; p=0.016).
Even with a modest number of adult volunteers, the consistency and linearity of regression slopes for all subjects were very good with excellent within-person Pearson correlations for the accelerometer versus ventilation volume data. Computing accelerometric standard deviations allowed good sensitivity for compliance assessments even for sedentary activities. These pilot findings supported the hypothesis that a common linear regression is likely to be usable for a wider range of adults to predict ventilation volumes from accelerometry data over a range of low to moderate energy level activities. The predicted volumes would then allow real-time estimates of potential dose, enabling more robust panel studies. The poorer correlation in predicting ventilation rate for an accelerometer located on the wrist suggested that this location should not be considered for predictions of ventilation volume.
Ventilation volume; personal exposure; potential dose; triaxial accelerometry; adults; wearing compliance
Physical activity patterns of a population remain mostly assessed by the questionnaires. However, few physical activity questionnaires have been validated in Asian populations. We previously utilized a combination of different questionnaires to assess leisure time, transportation, occupational and household physical activity in the Singapore Prospective Study Program (SP2). The International Physical Activity Questionnaire (IPAQ) has been developed for a similar purpose. In this study, we compared estimates from these two questionnaires with an objective measure of physical activity in a multi-ethnic Asian population.
Physical activity was measured in 152 Chinese, Malay and Asian Indian adults using an accelerometer over five consecutive days, including a weekend. Participants completed both the physical activity questionnaire in SP2 (SP2PAQ) and IPAQ long form. 43subjects underwent a second set of measurements on average 6 months later to assess reproducibility of the questionnaires and the accelerometer measurements. Spearman correlations were used to evaluate validity and reproducibility and correlations for validity were corrected for within-person variation of accelerometer measurements. Agreement between the questionnaires and the accelerometer measurements was also evaluated using Bland Altman plots.
The corrected correlation with accelerometer estimates of energy expenditure from physical activity was better for the SP2PAQ (vigorous activity: r = 0.73; moderate activity: r = 0.27) than for the IPAQ (vigorous activity: r = 0.31; moderate activity: r = 0.15). For moderate activity, the corrected correlation between SP2PAQ and the accelerometer was higher for Chinese (r = 0.38) and Malays (r = 0.57) than for Indians (r = -0.09). Both questionnaires overestimated energy expenditure from physical activity to a greater extent at higher levels of physical activity than at lower levels of physical activity. The reproducibility for moderate activity (accelerometer: r = 0.68; IPAQ: r = 0.58; SP2PAQ: r = 0.55) and vigorous activity (accelerometer: 0.52; IPAQ: r = 0.38; SP2PAQ: r = 0.75) was moderate to high for all instruments.
The agreement between IPAQ and accelerometer measurements of energy expenditure from physical activity was poor in our Asian study population. The SP2PAQ showed good validity and reproducibility for vigorous activity, but performed less well for moderate activity particularly in Indians. Further effort is needed to develop questionnaires that better capture moderate activity in Asian populations.
To test a field-based protocol using intermittent activities representative of children's physical activity behaviours, to generate behaviourally valid, population-specific accelerometer cut-points for sedentary behaviour, moderate, and vigorous physical activity.
Twenty-eight children (46% boys) aged 10–11 years wore a hip-mounted uniaxial GT1M ActiGraph and engaged in 6 activities representative of children's play. A validated direct observation protocol was used as the criterion measure of physical activity. Receiver Operating Characteristics (ROC) curve analyses were conducted with four semi-structured activities to determine the accelerometer cut-points. To examine classification differences, cut-points were cross-validated with free-play and DVD viewing activities.
Cut-points of ≤372, >2160 and >4806 counts•min−1 representing sedentary, moderate and vigorous intensity thresholds, respectively, provided the optimal balance between the related needs for sensitivity (accurately detecting activity) and specificity (limiting misclassification of the activity). Cross-validation data demonstrated that these values yielded the best overall kappa scores (0.97; 0.71; 0.62), and a high classification agreement (98.6%; 89.0%; 87.2%), respectively. Specificity values of 96–97% showed that the developed cut-points accurately detected physical activity, and sensitivity values (89–99%) indicated that minutes of activity were seldom incorrectly classified as inactivity.
The development of an inexpensive and replicable field-based protocol to generate behaviourally valid and population-specific accelerometer cut-points may improve the classification of physical activity levels in children, which could enhance subsequent intervention and observational studies.
Accelerometers are promising tools for characterizing physical activity (PA) patterns in free-living persons. To date, validation of energy expenditure (EE) predictions from accelerometers has been restricted to short laboratory or simulated free-living protocols. This study seeks to determine the capabilities of eight previously published regression equations for three commercially available accelerometers to predict summary measures of daily EE.
Methods and Procedures:
Study participants were outfitted with ActiGraph, Actical, and RT3 accelerometers, while measurements were simultaneously made during overnight stays in a room calorimeter, which provided minute-by-minute EE measurements, in a diverse subject population (n = 85). Regression equations for each device were used to predict the minute-by-minute metabolic equivalents (METs) along with the daily PA level (PAL).
Two RT3 regressions and one ActiGraph regression were not significantly different from calorimeter measured PAL. When data from the entire visit were divided into four intensity categories—sedentary, light, moderate, and vigorous—significant (P < 0.001) over- and underpredictions were detected in numerous regression equations and intensity categories.
Most EE prediction equations showed differences of <2% in the moderate and vigorous intensity categories. These differences, though small in magnitude, may limit the ability of these regressions to accurately characterize whether specific PA goals have been met in the field setting. New regression equations should be developed if more accurate prediction of the daily PAL or higher precision in determining the time spent in specific PA intensity categories is desired.
Previously the National Health and Examination Survey measured physical activity with an accelerometer worn on the hip for seven days, but recently changed the location of the monitor to the wrist.
This study compared estimates of physical activity intensity and type with an accelerometer on the hip versus the wrist.
Healthy adults (n=37) wore triaxial accelerometers (Wockets) on the hip and dominant wrist along with a portable metabolic unit to measure energy expenditure during 20 activities. Motion summary counts were created, then receiver operating characteristic (ROC) curves were used to determine sedentary and activity intensity thresholds. Ambulatory activities were separated from other activities using the coefficient of variation (CV) of the counts. Mixed model predictions were used to estimate activity intensity.
The ROC for determining sedentary behavior had greater sensitivity and specificity (71% and 96%) at the hip than the wrist (53% and 76%), as did the ROC for moderate to vigorous physical activity on the hip (70% and 83%) versus the wrist (30% and 69%). The ROC for the CV associated with ambulation had a larger AUC at the hip compared to the wrist (0.83 and 0.74). The prediction model for activity energy expenditure (AEE) resulted in an average difference of 0.55 (+/− 0.55) METs on the hip and 0.82 (+/− 0.93) METs on the wrist.
Methods frequently used for estimating AEE and identifying activity intensity thresholds from an accelerometer on the hip generally do better than similar data from an accelerometer on the wrist. Accurately identifying sedentary behavior from a lack of wrist motion presents significant challenges.
Activity monitor; accelerometer; mobile phones; thresholds; coefficient of variation; sedentary behavior; exercise
A recent review concludes that the agreement of data across ActiGraph accelerometer models for children and youth still is uncertain. The aim of this study was to evaluate the agreement of three generations of ActiGraph accelerometers in children in a free-living condition.
Sixteen 9-year-olds wore the ActiGraph AM7164, GT1M and GT3X+ simultaneously for three consecutive days. We compared mean counts per minute (mcpm) and time spent at different intensities from the three generations of monitors, and the agreement of outputs were evaluated by intra-class correlation coefficients (ICC) and Bland-Altman plots.
The ICC for mcpm was 0.985 (95% CI = 0.898, 0.996). We found a relative difference of 11.6% and 9.8% between the AM7164 and the GT1M and AM7164 and the GT3X+, respectively. The relative difference between mcpm assessed by the GT1M and GT3X+ was 1.7%. The inter-generation differences varied in magnitude and direction across intensity levels, with the largest difference found in the highest intensities.
We found that the ActiGraph model AM7164 yields higher outputs of mean physical activity intensity (mcpm) than the models GT1M and GT3X+ in children in free-living conditions. The generations GT1M and GT3X+ provided comparable outputs. The differences between the old and the newer monitors were more complex when investigating time spent at different intensities. Comparisons of data assessed by the AM7164 with data assessed by newer generations ActiGraphs should be done with caution.
Accelerometer; Physical activity; Children; Youth; Assessment
Socio-cultural differences for country-specific activities are rarely addressed in physical activity questionnaires. We examined the reliability and validity of the Indian Migration Study Physical Activity Questionnaire (IMS-PAQ) in urban and rural groups in India.
A sub-sample of IMS participants (n = 479) was used to examine short term (≤1 month [n = 158]) and long term (> 1 month [n = 321]) IMS-PAQ reliability for levels of total, sedentary, light and moderate/vigorous activity (MVPA) intensity using intraclass correlation (ICC) and kappa coefficients (k). Criterion validity (n = 157) was examined by comparing the IMS-PAQ to a uniaxial accelerometer (ACC) worn ≥4 days, via Spearman's rank correlations (ρ) and k, using Bland-Altman plots to check for systematic bias. Construct validity (n = 7,000) was established using linear regression, comparing IMS-PAQ against theoretical constructs associated with physical activity (PA): BMI [kg/m2], percent body fat and pulse rate.
IMS-PAQ reliability ranged from ICC 0.42-0.88 and k = 0.37-0.61 (≤1 month) and ICC 0.26 to 0.62; kappa 0.17 to 0.45 (> 1 month). Criterion validity was ρ = 0.18-0.48; k = 0.08-0.34. Light activity was underestimated and MVPA consistently and substantially overestimated for the IMS-PAQ vs. the accelerometer. Criterion validity was moderate for total activity and MVPA. Reliability and validity were comparable for urban and rural participants but lower in women than men. Increasing time spent in total activity or MVPA, and decreasing time in sedentary activity were associated with decreasing BMI, percent body fat and pulse rate, thereby demonstrating construct validity.
IMS-PAQ reliability and validity is similar to comparable self-reported instruments. It is an appropriate tool for ranking PA of individuals in India. Some refinements may be required for sedentary populations and women in India.
Health behaviour; Activity Domains; Low-Middle Income Countries; Reproducibility; Adults; Methodology
Accelerometers are considered to be the most promising tool for measuring physical activity (PA) in free-living young children. So far, no studies have examined the feasibility and validity of accelerometer measurements in children under 3 years of age. Therefore, the purpose of the present study was to examine the feasibility and validity of accelerometer measurements in toddlers (1- to 3-year olds).
Forty-seven toddlers (25 boys; 20 ± 4 months) wore a GT1M ActiGraph accelerometer for 6 consecutive days and parental perceptions of the acceptability of wearing the monitor were assessed to examine feasibility. To investigate the validity of the ActiGraph and the predictive validity of three ActiGraph cut points, accelerometer measurements of 31 toddlers (17 boys; 20 ± 4 months) during free play at child care were compared to directly observed PA, using the Observational System for Recording Physical Activity in Children-Preschool (OSRAC-P). Validity was assessed using Pearson and Spearman correlations and predictive validity using area under the Receiver Operating Characteristic curve (ROC-AUC).
The feasibility examination indicated that accelerometer measurements of 30 toddlers (63.8%) could be included with a mean registration time of 564 ± 62 min during weekdays and 595 ± 83 min during weekend days. According to the parental reports, 83% perceived wearing the accelerometer as 'not unpleasant and not pleasant' and none as 'unpleasant'. The validity evaluation showed that mean ActiGraph activity counts were significantly and positively associated with mean OSRAC-P activity intensity (r = 0.66; p < 0.001; n = 31). Further, the correlation among the ActiGraph activity counts and the OSRAC-P activity intensity level during each observation interval was significantly positive (ρ = 0.52; p < 0.001; n = 4218). Finally, the three sedentary cut points exhibited poor to fair classification accuracy (ROC-AUC: 0.56 to 0.71) while the three light PA (ROC-AUC: 0.51 to 0.62) and the three moderate-to-vigorous PA cut points (ROC-AUC: 0.53 to 0.57) demonstrated poor classification accuracy with respect to detecting sedentary behavior, light PA and moderate-to-vigorous PA, respectively.
The present findings suggest that ActiGraph accelerometer measurements are feasible and valid for quantifying PA in toddlers. However, further research is needed to accurately identify PA intensities in toddlers using accelerometry.
ActiGraph; Observational System for Recording Physical Activity in Children-Preschool (OSRAC-P); Feasibility; Criterion validity; Predictive validity; Accelerometer cut points; Toddlers
To determine the feasibility of using an accelerometer to characterize physical activity patterns (PA) surrounding chronic obstructive pulmonary disease (COPD) exacerbations (AECOPD) in patients with COPD for 16 weeks.
Patients with COPD (n = 8) wore the RT3®, a triaxial accelerometer (Stayhealthy, Monrovia, CA) during waking hours and kept daily symptom diaries. The mean vector magnitude unit (VMU) per minute was calculated by dividing the total VMU for the day by the number of minutes the device was worn. Descriptive statistics were used and plots were made showing PA for each subject with AECOPD markers based on symptom diaries and health resource utilization.
Sample characteristics were: age 71 ± 4; 5 Females; forced expiratory volume in one second (FEV1)% predicted: 40% ± 16%; FEV1/forced vital capacity: 45 ± 7; and Medical Research Council dyspnea scale: 2.3 ± 0.9. Overall adherence to the monitoring protocol was 97.6% (Range 92%–100%) while adherence to wearing the device for at least 10 hours per day was 91.5% (Range 75%–99%). Mean vector magnitude units per minute was 117.8 ± 47 (Range 61.4–184.1). Seven exacerbations were captured over a total of 896 person-days of monitoring. There were substantial intra-individual fluctuations in daily PA during both the stable state and with outpatient treated exacerbations.
Patients with COPD were able to adhere to a 16-week activity monitoring protocol and reported a willingness to wear such a device for an extended period of time if the data yield important and useful information for themselves and their health provider. Future work will need to focus first, on validating other promising devices that produce higher quality PA data and second, replicate this monitoring protocol with a larger sample of COPD patients over a longer period.
physical activity; accelerometry; COPD; exacerbations
We sought to develop procedures for computerized analysis of long-term, high-resolution activity monitoring data that allow accurate assessment of the time course of activity levels suitable for use in chronic obstructive pulmonary disease (COPD) patients. Twenty-two COPD patients utilizing long-term oxygen recruited from 5 sites of the COPD Clinical Research Network wore a triaxial accelerometer (RT3, Stayhealthy, Monrovia, CA) during waking hours over a14 day period. Computerized algorithms were composed allowing minute-by-minute activity data to be analyzed to determine, for each minute, whether the monitor was being worn. Temporal alignment allowed determination of average time course of activity level, expressed as average vector magnitude units (VMU, the vectorial sum of activity counts in three orthogonal directions) per minute, for each hour of the day. Mid-day activity was quantified as average VMU/minute between 10AM and 4PM for minutes the monitor was worn. Over the 14 day monitoring period, subjects wore the monitor an average of 11.4±3.0 hours·day−1. During midday hours, subjects wore the monitor 76.3% of the time and generated an average activity level of 112±55 VMU·min−1. Increase in precision of activity estimates with longer monitoring periods was demonstrated. This analysis scheme allows a detailed temporal pattern of activity to be defined from triaxial accelerometer recordings and has the potential to facilitate comparisons among subjects and between subject groups. This trial is registered at ClinicalTrials.gov (NCT00325754).
Chronic Obstructive Pulmonary Disease; Activity Monitor; Tri-axial Accelerometry; Daily Activities; Computerized Algorithms; Temporal Alignment