Ability of the Actiwatch Accelerometer to Predict Free-Living Energy Expenditure in Young Children

Total energy expenditure measured by doubly labeled water method in children and adolescents: a systematic review

Total energy expenditure (TEE) is essential for understanding the growth, development, and physical activity of children and adolescents. This study aimed to summarize the existing evidence on TEE measured using the doubly labeled water (DLW) technique in children and adolescents aged 1-18 years. Furthermore, this review compared TEE between obese and normal-weight participants. This systematic review used the PubMed, ScienceDirect, Web of Science, and EBSCO databases. These studies were limited to those published in English between January 2000 and December 2021. Articles presenting objectively measured data on the TEE of children and adolescents aged 1-18 years measured using the DLW method were included. Physical activity level (PAL; TEE/basal metabolic rate [BMR]) and BMR data were also obtained. The search strategy identified 2,351 articles, of which 63 (n=4,283 children and adolescents; 45.4% male) met the selection criteria. The participants in the 10 studies were overweight or obese (n=413). In our study, TEE increased in male and female participants aged 1-18 years. PAL increased with age in males (y=0.0272x+1.3887, r2=0.511) and females (y=0.0199x+1.401, r2=0.335), and the slope of PAL with age did not differ between males and females. The TEE of obese and overweight participants was higher than that of normal-weight participants, but the slope of TEE did not differ between normal-weight (y=132.99x+702.24, r2=0.877) and obese individuals (y=136.18x+1,037.9, r2=0.842). In conclusion, this review provides convincing evidence that daily TEE progressively increases with growth in males and females aged 1-18 years.

A systematic review of the validity, reliability, and feasibility of measurement tools used to assess the physical activity and sedentary behaviour of pre-school aged children

Physical activity (PA) and sedentary behaviour (SB) of pre-school aged children are associated with important health and developmental outcomes. Accurate measurement of these behaviours in young children is critical for research and practice in this area. The aim of this review was to examine the validity, reliability, and feasibility of measurement tools used to assess PA and SB of pre-school aged children.Searches of electronic databases, and manual searching, were conducted to identify articles that examined the measurement properties (validity, reliability or feasibility) of measurement tools used to examine PA and/or SB of pre-school aged children (3-7 years old). Following screening, data were extracted and risk of bias assessment completed on all included articles.A total of 69 articles, describing 75 individual studies were included. Studies assessed measurement tools for PA (n = 27), SB (n = 5), and both PA and SB (n = 43). Outcome measures of PA and SB differed between studies (e.g. moderate to vigorous activity, step count, posture allocation). Most studies examined the measurement properties of one measurement tool only (n = 65). Measurement tools examined included: calorimetry, direct observation, combined heart rate and accelerometry, heart rate monitors, accelerometers, pedometers, and proxy report (parent, carer or teacher reported) measures (questionnaires or diaries). Studies most frequently assessed the validity (criterion and convergent) (n = 65), face and content validity (n = 2), test-retest reliability (n = 10) and intra-instrument reliability (n = 1) of the measurement tools. Feasibility data was abstracted from 41 studies.Multiple measurement tools used to measure PA and SB in pre-school aged children showed some degree of validity, reliability and feasibility, but often for different purposes. Accelerometers, including the Actigraph (in particular GT3X versions), Actical, ActivPAL and Fitbit (Flex and Zip), and proxy reported measurement tools used in combination may be useful for a range of outcome measures, to measure intensity alongside contextual information.

Evaluation of the wrist-worn ActiGraph wGT3x-BT for estimating activity energy expenditure in preschool children

BACKGROUND/OBJECTIVES

Easy-to-use and accurate methods to assess free-living activity energy expenditure (AEE) in preschool children are required. The aims of this study in healthy preschool children were to (a) evaluate the ability of the wrist-worn ActiGraph wGT3x-BT to predict free-living AEE and (b) assess wear compliance using a 7-day, 24-h protocol.

SUBJECTS/METHODS

Participants were 40 Swedish children (5.5±0.2 years) in the Mobile-based intervention intended to stop obesity in preschoolers (MINISTOP) obesity prevention trial. Total energy expenditure (TEE) was assessed using the doubly labeled water method during 14 days. AEE was calculated as (TEEx0.9) minus predicted basal metabolic rate. The ActiGraph accelerometer was worn on the wrist for 7 days and outputs used were mean of the daily and awake filtered vector magnitude (mean VM total and mean VM waking).

RESULTS

The ActiGraph was worn for 7 (n=34, 85%), 6 (n=4, 10%), 5 (n=1, 2.5%) and 4 (n=1, 2.5%) days (a valid day was ⩾600 awake minutes). Alone, mean VM total and mean VM waking were able to explain 14% (P=0.009) and 24% (P=0.001) of the variation in AEE, respectively. By incorporating fat and fat-free mass in the models 58% (mean VM total) and 62% (mean VM waking) in the variation of AEE was explained (P<0.001).

CONCLUSIONS

The wrist-worn ActiGraph wGT3x-BT in combination with body composition variables explained up to the 62% of the variation in AEE. Given the high wear compliance, the wrist-worn ActiGraph has the potential to provide useful information in studies where physical activity in preschool children is measured.

Dietary prescription adherence and non-structured physical activity following weight loss with and without aerobic exercise

OBJECTIVES

To compare the effects of weight loss with and without exercise on 1) dietary prescription adherence and 2) non-structured activity in postmenopausal women.

DESIGN

Longitudinal study.

SETTING

Clinical research setting with facility based exercise and nutrition education.

PARTICIPANTS

Overweight and obese women, 45-76 years old.

INTERVENTION

6 months of weight loss alone (WL; N=38) or with aerobic exercise (AEX+WL; N=41).

MEASUREMENTS

Cardiorespiratory fitness (VO2max), resting metabolic rate (RMR), seven day food intake, and physical activity (by Actical accelerometers worn in a subset subgroup: WL: N=10; AEX+WL: N=15) were assessed before and after the interventions.

RESULTS

Both interventions resulted in similar weight loss (~9%) and no significant changes in RMR, while only the AEX+WL group improved VO2max (~10%). At baseline, the AEX+WL group consumed slightly more protein than the WL group (P<0.01). Macronutrient intake did not change following AEX+WL, but the WL group decreased their fat intake and increased their carbohydrates and protein intakes (Ps<0.05), which resulted in similar macronutrient intakes between groups post-intervention. Weekday total activity counts decreased 22% (P<0.05) following WL. This change tended (P=0.07) to be different than the lack of change in non-structured activity observed following AEX+WL.

CONCLUSION

Although similar dietary adherence was observed, these data suggest that postmenopausal women undergoing weight loss may benefit from the addition of exercise to prevent the decline in non-structured activity observed following weight loss alone.

Evaluation of Actiheart and a 7 d activity diary for estimating free-living total and activity energy expenditure using criterion methods in 1·5- and 3-year-old children

Accurate and easy-to-use methods to assess free-living energy expenditure in response to physical activity in young children are scarce. In the present study, we evaluated the capacity of (1) 4 d recordings obtained using the Actiheart (mean heart rate (mHR) and mean activity counts (mAC)) to provide assessments of total energy expenditure (TEE) and activity energy expenditure (AEE) and (2) a 7 d activity diary to provide assessments of physical activity levels (PAL) using three sets of metabolic equivalent (MET) values (PALTorun, PALAdolphand PALAinsworth) in forty-four and thirty-one healthy Swedish children aged 1·5 and 3 years, respectively. Reference TEE, PALrefand AEE were measured using criterion methods, i.e. the doubly labelled water method and indirect calorimetry. At 1·5 years of age, mHR explained 8 % (P= 0·006) of the variation in TEE above that explained by fat mass and fat-free mass. At 3 years of age, mHR and mAC explained 8 (P= 0·004) and 6 (P= 0·03) % of the variation in TEE and AEE, respectively, above that explained by fat mass and fat-free mass. At 1·5 and 3 years of age, average PALAinsworthvalues were 1·44 and 1·59, respectively, and not significantly different from PALrefvalues (1·39 and 1·61, respectively). By contrast, average PALTorun(1·5 and 3 years) and PALAdolph(3 years) values were lower (P< 0·05) than the corresponding PALrefvalues. In conclusion, at both ages, Actiheart recordings explained a small but significant fraction of free-living energy expenditure above that explained by body composition variables, and our activity diary produced mean PAL values in agreement with reference values when using MET values published by Ainsworth.

Validation of the TracmorD triaxial accelerometer to assess physical activity in preschool children

OBJECTIVES

To assess validity evidence of TracmorD to determine energy used for physical activity in 3-4-year-old children.

DESIGN AND METHODS

Participants were randomly selected from GECKO Drenthe cohort (n = 30, age 3.4 ± 0.3 years). Total energy expenditure (TEE) was measured using the doubly labeled water method. Sleeping metabolic rate (SMR) was measured by indirect calorimetry (Deltatrac). TEE and SMR were used to calculate physical activity level (PAL) and activity energy expenditure (AEE). Physical activity was monitored using a DirectLife triaxial accelerometer, TracmorD with activity counts per minute (ACM) and activity counts per day (ACD) as outcome measures.

RESULTS

The best predictor for PAL was ACM with gender and weight, the best predictor for AEE was ACM alone (backward regression, R(2) = 0.50, P = 0.010 and R2 = 0.31, P = 0.011, respectively). With ACD, the prediction model for PAL included ACD, height, gender, and sleep duration (R2 = 0.48, P = 0.033), the prediction model for AEE included ACD, gender and sleep duration (R2 = 0.39, P = 0.042). The accelerometer was worn for 5 days, but 3 days did not give a different estimated PAL.

CONCLUSION

TracmorD provides moderate-to-strong validity evidence that supports its use to evaluate energy used for physical activity in 3-4-year-old children.

Objectively measured sedentary time, physical activity and markers of body fat in preschool children

The purpose of this study was to examine the associations between sedentary behavior and moderate to vigorous physical activity (MVPA), measured by accelerometry, with body mass index (BMI) and waist circumference in 357 preschool children. Linear mixed models were used adjusting for race/ethnicity, parental education, and preschool. Follow-up analyses were performed using quantile regression. Among boys, MVPA was positively associated with BMI z-score (b = 0.080, p = .04) but not with waist circumference; quantile regression showed that MVPA was positively associated with BMI z-score at the 50th percentile (b = 0.097, p < .05). Among girls, no associations were observed between sedentary behavior and MVPA in relation to mean BMI z-score and mean waist circumference. Quantile regression indicated that, among girls at the 90th waist circumference percentile, a positive association was found with sedentary behavior (b = 0.441, p < .05), and a negative association was observed with MVPA (b = -0.599, p < .05); no associations were found with BMI z-score. In conclusion, MVPA was positively associated with BMI z-score among boys, and MVPA was negatively associated and sedentary behavior was positively associated with waist circumference among girls at the 90th percentile.

Cross-sectional time series and multivariate adaptive regression splines models using accelerometry and heart rate predict energy expenditure of preschoolers

Prediction equations of energy expenditure (EE) using accelerometers and miniaturized heart rate (HR) monitors have been developed in older children and adults but not in preschool-aged children. Because the relationships between accelerometer counts (ACs), HR, and EE are confounded by growth and maturation, age-specific EE prediction equations are required. We used advanced technology (fast-response room calorimetry, Actiheart and Actigraph accelerometers, and miniaturized HR monitors) and sophisticated mathematical modeling [cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS)] to develop models for the prediction of minute-by-minute EE in 69 preschool-aged children. CSTS and MARS models were developed by using participant characteristics (gender, age, weight, height), Actiheart (HR+AC_x) or ActiGraph parameters (AC_x, AC_y, AC_z, steps, posture) [x, y, and z represent the directional axes of the accelerometers], and their significant 1- and 2-min lag and lead values, and significant interactions. Relative to EE measured by calorimetry, mean percentage errors predicting awake EE (-1.1 ± 8.7%, 0.3 ± 6.9%, and -0.2 ± 6.9%) with CSTS models were slightly higher than with MARS models (-0.7 ± 6.0%, 0.3 ± 4.8%, and -0.6 ± 4.6%) for Actiheart, ActiGraph, and ActiGraph+HR devices, respectively. Predicted awake EE values were within ±10% for 81-87% of individuals for CSTS models and for 91-98% of individuals for MARS models. Concordance correlation coefficients were 0.936, 0.931, and 0.943 for CSTS EE models and 0.946, 0.948, and 0.940 for MARS EE models for Actiheart, ActiGraph, and ActiGraph+HR devices, respectively. CSTS and MARS models should prove useful in capturing the complex dynamics of EE and movement that are characteristic of preschool-aged children.

Genetic admixture, social-behavioural factors and body composition are associated with blood pressure differently by racial-ethnic group among children

Cardiovascular disease has a progressively earlier age of onset, and disproportionately affects African Americans (AAs) in the United States. It has been difficult to establish the extent to which group differences are due to physiological, genetic, social or behavioural factors. In this study, we examined the association between blood pressure and these factors among a sample of 294 children, identified as AA, European American or Hispanic American. We use body composition, behavioural (diet and physical activity) and survey-based measures (socio-economic status and perceived racial discrimination), as well as genetic admixture based on 142 ancestry informative markers (AIMs) to examine associations with systolic and diastolic blood pressure. We find that associations differ by ethnic/racial group. Notably, among AAs, physical activity and perceived racial discrimination, but not African genetic admixture, are associated with blood pressure, while the association between blood pressure and body fat is nearly absent. We find an association between blood pressure and an AIM near a marker identified by a recent genome-wide association study. Our findings shed light on the differences in risk factors for elevated blood pressure among ethnic/racial groups, and the importance of including social and behavioural measures to grasp the full genetic/environmental aetiology of disparities in blood pressure.

Accelerometer use with children, older adults, and adults with functional limitations

Accurately assessing physical activity behavior in children, older adults, and adults with functional limitations is essential to further our understanding of determinants of physical activity behavior in these populations and to design, implement, and evaluate interventions designed to increase physical activity participation. Objective methods to assess physical activity behavior, owing to improvements in accuracy and precision over self-report measures, have become common in research and practice settings. This article reviews the current use of objective methods to assess physical activity in observational, determinant, and intervention studies for children, older adults, and adults with functional limitations. Important considerations are presented when adopting prediction algorithms developed on one population, and using in another population that is markedly different in age, health, and functional status. Best practices are presented, along with future recommendations for research to advance this area of scientific inquiry.

Validation of cross-sectional time series and multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents using doubly labeled water

Accurate, nonintrusive, and inexpensive techniques are needed to measure energy expenditure (EE) in free-living populations. Our primary aim in this study was to validate cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on observable participant characteristics, heart rate (HR), and accelerometer counts (AC) for prediction of minute-by-minute EE, and hence 24-h total EE (TEE), against a 7-d doubly labeled water (DLW) method in children and adolescents. Our secondary aim was to demonstrate the utility of CSTS and MARS to predict awake EE, sleep EE, and activity EE (AEE) from 7-d HR and AC records, because these shorter periods are not verifiable by DLW, which provides an estimate of the individual's mean TEE over a 7-d interval. CSTS and MARS models were validated in 60 normal-weight and overweight participants (ages 5-18 y). The Actiheart monitor was used to simultaneously measure HR and AC. For prediction of TEE, mean absolute errors were 10.7 +/- 307 kcal/d and 18.7 +/- 252 kcal/d for CSTS and MARS models, respectively, relative to DLW. Corresponding root mean square error values were 305 and 251 kcal/d for CSTS and MARS models, respectively. Bland-Altman plots indicated that the predicted values were in good agreement with the DLW-derived TEE values. Validation of CSTS and MARS models based on participant characteristics, HR monitoring, and accelerometry for the prediction of minute-by-minute EE, and hence 24-h TEE, against the DLW method indicated no systematic bias and acceptable limits of agreement for pediatric groups and individuals under free-living conditions.

Validity and reproducibility of motion sensors in youth: a systematic update

PURPOSE

To review recently published studies on the reproducibility, validity, and feasibility of motion sensors used to assess physical activity in healthy children and adolescents (2-18 yr).

METHODS

On October 2004, a systematic literature search in PubMed, EMBASE, and PsycINFO was performed. This search has been updated on October 2007. In this update, the clinimetric quality of three pedometers (Digi-Walker, Walk4Life, and Sun TrekLINQ) and nine accelerometers (ActiGraph, BioTrainer, StepWatch Activity Monitor, Actiwatch, Actical, Tritrac-R3D, RT3, ActivTracer, and Mini-Motionlogger) has been evaluated and compared using a checklist.

RESULTS

Thirty-two recently published clinimetric studies have been reviewed. All 12 motion sensors have been validated in youth in one or more studies. There is strong evidence for moderate validity of the StepWatch in children and adolescents (4-18 yr) and moderate to good validity of the ActiGraph in preschool children and young children (2-8 yr). There is less evidence for the reproducibility and feasibility of the 12 motion sensors. Strong evidence exists for good reproducibility of the ActiGraph in preschool children (2-4 yr).

CONCLUSION

Compared to the review performed in 2004, there is increased evidence for the clinimetric quality of pedometers and accelerometers in youth. Most motion sensors seem reproducible, valid, and feasible in assessing physical activity in youth.

Physical activity in children measured by accelerometry: stability over time

The aim of this study was to examine the stability of objectively measured physical activity in Swedish children and to study variables that predicted physical activity and body mass index standard deviation score (BMI SDS) at follow-up. A total of 97 children provided valid repeated measurements of physical activity between 2002 and 2005. The children were on average 7.5 years at baseline (SD+/-0.92) and 9 years at follow-up (SD+/-0.92). The mean follow-up time was approximately 1.5 years (mean 558 days, SD+/-224). An accelerometer (Actiwatch, Cambridge Neurotechnology Ltd., Cambridge, UK) was used to measure physical activity during 7 consecutive days. Yearly weight and height were examined and BMI SDS was calculated. Baseline physical activity was significantly correlated with physical activity at follow-up (r=0.59) with a stronger correlation for boys (r=0.72) than for girls (r=0.51). High physical activity levels were more stable (r=0.74) than low physical activity levels (r=0.55). Physical activity at follow-up was explained by physical activity at baseline and season (R(2)=0.46) whereas BMI SDS at follow-up was explained by BMI SDS at baseline and age (R(2)=0.90). The results of this study suggest that physical activity levels are fairly stable in 6-10-year-old children.

Methodological considerations in using accelerometers to assess habitual physical activity in children aged 0-5 years

This paper reviews the evidence behind the methodological decisions accelerometer users make when assessing habitual physical activity in children aged 0-5 years. The purpose of the review is to outline an evidence-guided protocol for using accelerometry in young children and to identify gaps in the evidence base where further investigation is required. Studies evaluating accelerometry methodologies in young children were reviewed in two age groups (0-2 years and 3-5 years) to examine: (i) which accelerometer should be used, (ii) where the accelerometer should be placed, (iii) which epoch should be used, (iv) how many days of monitoring are required, (v) how many minutes of monitoring per day are required, (vi) how data should be reduced, (vii) which cut-point definitions for identifying activity intensity should be used, and (viii) which physical activity outcomes should be reported and how. Critique of the available evidence provided a basis for the development of a recommended users protocol in 3-5-year olds, although several issues require further research. Because of the absence of methodological studies in children under 3 years, a protocol for the use of accelerometers in this age range could not be specified. Formative studies examining the utility, feasibility and validity of accelerometer-based physical activity assessments are required in children under 3 years of age. Recommendations for further research are outlined, based on the above findings, which, if undertaken, will enhance the accuracy of accelerometer-based assessments of habitual physical activity in young children.

Validity of resting energy expenditure estimated by an activity monitor compared to indirect calorimetry

The use of activity monitors (triaxial accelerometers) to estimate total energy expenditure in kilocalories is dependent on the estimation of resting energy expenditure (REE). However, the REE estimated by activity monitors has not been validated against more precise techniques, such as indirect calorimetry (IC). Therefore, the objective of the present study was to compare REE estimated by the Actical activity monitor (ActMon) to that measured by IC and standard prediction equations of REE. Fifty healthy adults between 18 and 43 years of age were measured for weight and percentage of body fat using a digital scale and bioelectrical impedance. The REE estimated by the ActMon was only 129 kJ/d higher, but not statistically different (P>0·05), than the REE measured with IC. Using multiple linear regression, there was a positive relationship for men, but not for women, between fat mass (kg) and percentage of body fat and the difference in REE estimated by the ActMon compared to IC (P < 0·001). Therefore, in the cohort studied, the use of an activity monitor to estimate REE is valid when compared to IC, but not to a standard prediction equation of REE.

Physical activity in preschoolers: understanding prevalence and measurement issues

Accurate physical activity quantification in preschoolers is essential to establish physical activity prevalence, dose-response relationships between activity and health outcomes, and intervention effectiveness. To date, best practice approaches for physical activity measurement in preschool-aged children have been relatively understudied. This article provides a review of physical activity measurement tools for preschoolers, an overview of measurement of preschoolers' physical activity, and directions for further research. Electronic and manual literature searches were used to identify 49 studies that measured young children's physical activity, and 32 studies that assessed the validity and/or reliability of physical activity measures with preschool-aged children. While no prevalence data exist, measurement studies indicate that preschool children exhibit low levels of vigorous activity and high levels of inactivity, boys are more active than girls, and activity patterns tend to be sporadic and omnidirectional. As such, measures capable of capturing differing activity intensities in very short timeframes and over multiple planes are likely to have the most utility with this population. Accelerometers are well suited for this purpose, and a number of models have been used to objectively quantify preschoolers' physical activity. Only one model of pedometer has been investigated for validity with preschool-aged children, showing equivocal results. Direct observation of physical activity can provide detailed contextual information on preschoolers' physical activity, but is subjective and impractical for understanding daily physical activity. Proxy-report questionnaires are unlikely to be useful for determining actual physical activity levels of young children, and instead may be useful for identifying potential correlates of activity. Establishing validity is challenging due to the absence of a precise physical activity measure, or 'criterion', for young children. Both energy expenditure (EE) and direct observation have been considered criterion measures in the literature; however, EE is influenced by multiple variables, so its use as a physical activity 'criterion' is not ideal. Also, direct observation is inherently subjective, and coding protocols may result in failure to capture intermittent activity, thereby limiting its utility as a physical activity criterion. Accordingly, these issues must be taken into account where EE or direct observation are used to validate physical activity instruments. A combination of objective monitoring and direct observation may provide the best standard for the assessment of physical activity measurement tools. Ideally, the convergent validity of various physical activity tools should be investigated to determine the level of agreement between currently available measures. The correlational approaches commonly employed in the assessment of physical activity measures do not reveal this relationship, and can conceal potential bias of either measure.

Early rising children are more active than late risers

BACKGROUND

A low level of physical activity impacts mental as well as physical health. This study investigated the daily lifestyle habits that affect physical activity in young children.

METHODS

The relationship between physical activity, assessed by means of a Mini-Mitter Actiwatch device, and observed daily lifestyle habits was analyzed for 204 children, aged 12 to 40 months (average: 22.6 months), for whom 6-consecutive-day data from both the Actiwatch and sleep log were obtained.

RESULTS

An older age, male gender, and early waking time showed significant positive correlations with physical activity level. Multiple regression analysis revealed that these three variables were significant predictors of physical activity.

CONCLUSION

Promoting an early rising time is suggested to be an important element of cultivating good health in young children.

Physical activity assessment with accelerometers: an evaluation against doubly labeled water

This review focuses on the ability of different accelerometers to assess daily physical activity as compared with the doubly labeled water (DLW) technique, which is considered the gold standard for measuring energy expenditure under free-living conditions. The PubMed Central database (U.S. NIH free digital archive of biomedical and life sciences journal literature) was searched using the following key words: doubly or double labeled or labeled water in combination with accelerometer, accelerometry, motion sensor, or activity monitor. In total, 41 articles were identified, and screening the articles' references resulted in one extra article. Of these, 28 contained sufficient and new data. Eight different accelerometers were identified: 3 uniaxial (the Lifecorder, the Caltrac, and the CSA/MTI/Actigraph), one biaxial (the Actiwatch AW16), 2 triaxial (the Tritrac-R3D and the Tracmor), one device based on two position sensors and two motion sensors (ActiReg), and the foot-ground contact pedometer. Many studies showed poor results. Only a few mentioned partial correlations for accelerometer counts or the increase in R(2) caused by the accelerometer. The correlation between the two methods was often driven by subject characteristics such as body weight. In addition, standard errors or limits of agreement were often large or not presented. The CSA/MTI/Actigraph and the Tracmor were the two most extensively validated accelerometers. The best results were found for the Tracmor; however, this accelerometer is not yet commercially available. Of those commercially available, only the CSA/MTI/Actigraph has been proven to correlate reasonably with DLW-derived energy expenditure.

Accelerometers and pedometers: methodology and clinical application

PURPOSE OF REVIEW

The relationship between physical activity and health varies considerably, partly due to the difficulty of assessing physical activity accurately. This review examines recent literature on the validation of movement sensors to assess habitual physical activity. Recommendations are given for the use of movement sensors during free-living conditions and methods of data analysis and interpretation are discussed.

RECENT FINDINGS

Recent progress in physical-activity research includes detailed comparative studies of different monitor brands. The move away from using linear-regression equations and the use of novel data-analysis strategies is increasing the accuracy with which energy expenditure can be estimated from accelerometry. New technologies, including the combination of accelerometry with the measurement of physiological parameters, have great potential for the increased accuracy of physical-activity assessment.

SUMMARY

Accelerometry is able to adequately assess physical activity and its association with health outcomes but currently methods have limited accuracy for the estimation of free-living energy expenditure. Pedometers provide an inexpensive overall measure of physical activity but are unable to assess intensity, frequency and duration of activity or to estimate energy expenditure. Interpretation of monitor output is best kept as close to the measurement domain as possible.

Reliability of physical activity measures from accelerometry among preschoolers in free-living conditions

BACKGROUND & AIMS

Physical activity (PA) is a major determinant of obesity. Accelerometers have been reported to provide valid measures among adults. However, studies among preschoolers rarely report positive findings. To assess the day-to-day variability of accelerometers in preschoolers.

METHODS

Uni-axial accelerometer (Actigraph monitor AM 7164-2.2) counts including one weekend from the time of getting up in the morning until bedtime.

RESULTS

On average, boys showed 899 counts per minute (cpm) compared to 764 for girls (p<0.01; overall mean 828 cpm). Intra-individual correlation for accelerometry data between single days of examination was low with Pearson correlation coefficients between r=0.31 and 0.51. Furthermore, child's body mass index (BMI) and accelerometer measures were not related to each other (Pearson's correlation coefficient r=-0.06). Subsequent analyses showed higher measures (+50%cpm; p<0.01) for instruments placed in front of the umbilicus compared to instruments placed at the right hip.

CONCLUSIONS

Measurements of uni-axial accelerometers showed a low reliability among preschoolers. Uni-axial accelerometers placed on elastic belts might measure PA with low precision among preschoolers under free-living conditions possibly due to slipping instruments. This might explain lacking findings of an association between PA and obesity in studies among preschoolers.

Calibration of Accelerometer Output for Children

Understanding the determinants of physical activity behavior in children and youths is essential to the design and implementation of intervention studies to increase physical activity. Objective methods to assess physical activity behavior using various types of motion detectors have been recommended as an alternative to self-report for this population because they are not subject to many of the sources of error associated with children's recall required for self-report measures. This paper reviews the calibration of four different accelerometers used most frequently to assess physical activity and sedentary behavior in children. These accelerometers are the ActiGraph, Actical, Actiwatch, and the RT3 Triaxial Research Tracker. Studies are reviewed that describe the regression modeling approaches used to calibrate these devices using directly measured energy expenditure as the criterion. Point estimates of energy expenditure or count ranges corresponding to different activity intensities from several studies are presented. For a given accelerometer, the count cut points defining the boundaries for 3 and 6 METs vary substantially among the studies reviewed even though most studies include walking, running and free-living activities in the testing protocol. Alternative data processing using the raw acceleration signal is recommended as a possible alternative approach where the actual acceleration pattern is used to characterize activity behavior. Important considerations for defining best practices for accelerometer calibration in children and youths are presented.