Estimates Of Physical Activity Energy Expenditure Research Articles

Methods to Estimate Energy Expenditure, Physical Activity, and Sedentary Time in Pregnant Women: A Validation Study Using Doubly Labeled Water

Background: Activity trackers and the Pregnancy Physical Activity Questionnaire (PPAQ) measures physical activity (PA) and sedentary time (SED). However, none of these tools have been validated against a criterion method in pregnancy. We aimed to compare a consumer activity tracker and the Danish version of PPAQ (PPAQ-DK) and to validate them using the doubly labeled water technique (DLW) as criterion method. Methods: A total of 220 healthy pregnant women participated. Total energy expenditure (TEE), PA energy expenditure (PAEE), and PA level were determined at gestational Weeks 28–29 using DLW and a Garmin Vivosport (Garmin, Olathe, KS) activity tracker. In addition, PAEE, moderate-to-vigorous intensity PA, and SED were determined using the activity tracker and PPAQ-DK during all three trimesters. Results: TEE from the activity tracker and DLW correlated (r = .63; p < .001), but the activity tracker overestimated TEE (503 kcal/day). Also, the activity tracker overestimated PAEE (303 kcal/day) and PA level compared with DLW. Likewise, PPAQ-DK overestimated PAEE (1,513 kcal/day) compared with DLW. Compared to PPAQ-DK, the activity tracker reported lower values of PAEE and moderate-to-vigorous intensity PA and higher values of SED during all three trimesters. Conclusions: When compared to DLW, we found better agreement of PAEE estimates from the activity tracker than from PPAQ-DK. TEE from the tracker and DLW correlated moderately well, but this was not the case for PAEE or PA level. The activity tracker measured lower PA and higher SED than PPAQ-DK throughout pregnancy. The consumer activity tracker performed better than the questionnaire, but both significantly overestimated PA compared to DLW.

Validation of ambulatory monitoring devices to measure energy expenditure and heart rate in a military setting

Objectives.To investigate the validity of different devices and algorithms used in military organizations worldwide to assess physical activity energy expenditure (PAEE) and heart rate (HR) among soldiers. Design. Device validation study. Methods. Twenty-three male participants serving their mandatory military service accomplished, firstly, nine different military specific activities indoors, and secondly, a normal military routine outdoors. Participants wore simultaneously an ActiHeart, Everion, MetaMax 3B, Garmin Fenix 3, Hidalgo EQ02, and PADIS 2.0 system. The PAEE and HR data of each system were compared to the criterion measures MetaMax 3B and Hidalgo EQ02, respectively. Results. Overall, the recorded systematic errors in PAEE estimation ranged from 0.1 (±1.8) kcal.min−1 to −1.7 (±1.8) kcal.min−1 for the systems PADIS 2.0 and Hidalgo EQ02 running the Royal Dutch Army algorithm, respectively, and in the HR assessment ranged from −0.1 (±2.1) b.min−1 to 0.8 (±3.0) b.min−1 for the PADIS 2.0 and ActiHeart systems, respectively. The mean absolute percentage error (MAPE) in PAEE estimation ranged from 29.9% to 75.1%, with only the Everion system showing an overall MAPE <30%, but all investigated devices reported overall MAPE <1.4% in the HR assessment. Conclusions. The present study demonstrated poor to moderate validity in terms of PAEE estimation, but excellent validity in all investigated devices in terms of HR assessment. Overall, the Everion performed among the best in both parameters and with a device placement on the upper arm, the Everion system is particularly useful during military service, as it does not interfere with other relevant equipment.

Accuracy of 12 Wearable Devices for Estimating Physical Activity Energy Expenditure Using a Metabolic Chamber and the Doubly Labeled Water Method: Validation Study.

BackgroundSelf-monitoring using certain types of pedometers and accelerometers has been reported to be effective for promoting and maintaining physical activity (PA). However, the validity of estimating the level of PA or PA energy expenditure (PAEE) for general consumers using wearable devices has not been sufficiently established.ObjectiveWe examined the validity of 12 wearable devices for determining PAEE during 1 standardized day in a metabolic chamber and 15 free-living days using the doubly labeled water (DLW) method.MethodsA total of 19 healthy adults aged 21 to 50 years (9 men and 10 women) participated in this study. They followed a standardized PA protocol in a metabolic chamber for an entire day while simultaneously wearing 12 wearable devices: 5 devices on the waist, 5 on the wrist, and 2 placed in the pocket. In addition, they spent their daily lives wearing 12 wearable devices under free-living conditions while being subjected to the DLW method for 15 days. The PAEE criterion was calculated by subtracting the basal metabolic rate measured by the metabolic chamber and 0.1×total energy expenditure (TEE) from TEE. The TEE was obtained by the metabolic chamber and DLW methods. The PAEE values of wearable devices were also extracted or calculated from each mobile phone app or website. The Dunnett test and Pearson and Spearman correlation coefficients were used to examine the variables estimated by wearable devices.ResultsOn the standardized day, the PAEE estimated using the metabolic chamber (PAEEcha) was 528.8±149.4 kcal/day. The PAEEs of all devices except the TANITA AM-160 (513.8±135.0 kcal/day; P>.05), SUZUKEN Lifecorder EX (519.3±89.3 kcal/day; P>.05), and Panasonic Actimarker (545.9±141.7 kcal/day; P>.05) were significantly different from the PAEEcha. None of the devices was correlated with PAEEcha according to both Pearson (r=−.13 to .37) and Spearman (ρ=−.25 to .46) correlation tests. During the 15 free-living days, the PAEE estimated by DLW (PAEEdlw) was 728.0±162.7 kcal/day. PAEE values of all devices except the Omron Active style Pro (716.2±159.0 kcal/day; P>.05) and Omron CaloriScan (707.5±172.7 kcal/day; P>.05) were significantly underestimated. Only 2 devices, the Omron Active style Pro (r=.46; P=.045) and Panasonic Actimarker (r=.48; P=.04), had significant positive correlations with PAEEdlw according to Pearson tests. In addition, 3 devices, the TANITA AM-160 (ρ=.50; P=.03), Omron CaloriScan (ρ=.48; P=.04), and Omron Active style Pro (ρ=.48; P=.04), could be ranked in PAEEdlw.ConclusionsMost wearable devices do not provide comparable PAEE estimates when using gold standard methods during 1 standardized day or 15 free-living days. Continuous development and evaluations of these wearable devices are needed for better estimations of PAEE.

Usefulness of motion sensors to estimate energy expenditure in children and adults: a narrative review of studies using DLW.

It is well documented that meeting moderate-to-vigorous physical activity guidelines of 150 min per week is protective against chronic disease, and this is likely explained by higher energy expenditure (EE). In opposition, sedentary behavior (low EE) seems to impair health outcomes. There are gold standard methods to measure EE such as the doubly labeled water (DLW) or calorimetry. These methods are highly expensive and rely on complex techniques. Motion sensors present a good alternative to estimate EE and have been validated against these reference methods. This review summarizes findings from previous reviews and the most recently published studies on the validity of different motion sensors to estimate physical activity energy expenditure (PAEE) and total energy expenditure (TEE) against DLW, and whether adding other indicators may improve these estimations in children and adults. Regardless of the recognized validity of motion sensors to estimate PAEE and TEE at the group level, individual bias is very high even when combining biometric or physiological indicators. In children, accelerometers explained 13% of DLW's PAEE variance and 31% of TEE variance. In adults, DLW's explained variance was higher, 29 and 44% for PAEE and TEE, respectively. There is no ideal device, but identifying postures seems to be relevant for both children and adults' PAEE estimates. The variance associated with the number of methodological choices that these devices require invite investigators to work with the raw data in order to standardize all these procedures and potentiate the accelerometer signal-derived information. Models that consider biometric covariates seem only to improve TEE estimations, but adding heart rate enhances PAEE estimations in both children and adults.

Estimation of Physical Activity Energy Expenditure during Free-Living from Wrist Accelerometry in UK Adults.

BackgroundWrist-worn accelerometers are emerging as the most common instrument for measuring physical activity in large-scale epidemiological studies, though little is known about the relationship between wrist acceleration and physical activity energy expenditure (PAEE).Methods1695 UK adults wore two devices simultaneously for six days; a combined sensor and a wrist accelerometer. The combined sensor measured heart rate and trunk acceleration, which was combined with a treadmill test to yield a signal of individually-calibrated PAEE. Multi-level regression models were used to characterise the relationship between the two time-series, and their estimations were evaluated in an independent holdout sample. Finally, the relationship between PAEE and BMI was described separately for each source of PAEE estimate (wrist acceleration models and combined-sensing).ResultsWrist acceleration explained 44–47% between-individual variance in PAEE, with RMSE between 34–39 J•min-1•kg-1. Estimations agreed well with PAEE in cross-validation (mean bias [95% limits of agreement]: 0.07 [-70.6:70.7]) but overestimated in women by 3% and underestimated in men by 4%. Estimation error was inversely related to age (-2.3 J•min-1•kg-1 per 10y) and BMI (-0.3 J•min-1•kg-1 per kg/m2). Associations with BMI were similar for all PAEE estimates (approximately -0.08 kg/m2 per J•min-1•kg-1).ConclusionsA strong relationship exists between wrist acceleration and PAEE in free-living adults, such that irrespective of the objective method of PAEE assessment, a strong inverse association between PAEE and BMI was observed.

Cross-Sectional Associations of Objectively-Measured Physical Activity and Sedentary Time with Body Composition and Cardiorespiratory Fitness in Mid-Childhood: The PANIC Study

BackgroundThe minimum intensity of physical activity (PA) that is associated with favourable body composition and cardiorespiratory fitness (CRF) remains unknown.ObjectiveTo investigate cross-sectional associations of PA and sedentary time (ST) with body composition and CRF in mid-childhood.MethodsPA, ST, body composition and CRF were measured in a population-based sample of 410 children (aged 7.6 ± 0.4 years). Combined heart-rate and movement sensing provided estimates of PA energy expenditure (PAEE, kJ/kg/day) and time (min/day) at multiple fine-grained metabolic equivalent (MET) levels, which were also collapsed to ST and light PA (LPA), moderate PA (MPA) and vigorous PA (VPA). Fat mass index (FMI, kg/m2), trunk fat mass index (TFMI, kg/m2) and fat-free mass index (FFMI, kg/m2.5) were derived from dual-energy X-ray absorptiometry. Maximal workload from a cycle ergometer test provided a measure of CRF (W/kg FFM). Linear regression and isotemporal substitution models were used to investigate associations.ResultsThe cumulative time above 2 METs (221 J/min/kg) was inversely associated with FMI and TFMI in both sexes (p < 0.001) whereas time spent above 3 METs was positively associated with CRF (p ≤ 0.002); CRF increased and adiposity decreased dose-dependently with increasing MET levels. ST was positively associated with FMI and TFMI (p < 0.001) but there were inverse associations between all PA categories (including LPA) and adiposity (p ≤ 0.002); the magnitude of these associations depended on the activity being displaced in isotemporal substitution models but were consistently stronger for VPA. PAEE, MPA and to a greater extent VPA, were all positively related to CRF (p ≤ 0.001).ConclusionsPA exceeding 2 METs is associated with lower adiposity in mid-childhood, whereas PA of 3 METs is required to benefit CRF. VPA was most beneficial for fitness and fatness, from a time-for-time perspective, but displacing any lower-for-higher intensity may be an important first-order public health strategy.Clinical trial registry number (website): NCT01803776 (https://clinicaltrials.gov/ct2/show/NCT01803776).

Validity and reliability of Nike + Fuelband for estimating physical activity energy expenditure.

BackgroundThe Nike + Fuelband is a commercially available, wrist-worn accelerometer used to track physical activity energy expenditure (PAEE) during exercise. However, validation studies assessing the accuracy of this device for estimating PAEE are lacking. Therefore, this study examined the validity and reliability of the Nike + Fuelband for estimating PAEE during physical activity in young adults. Secondarily, we compared PAEE estimation of the Nike + Fuelband with the previously validated SenseWear Armband (SWA).MethodsTwenty-four participants (n = 24) completed two, 60-min semi-structured routines consisting of sedentary/light-intensity, moderate-intensity, and vigorous-intensity physical activity. Participants wore a Nike + Fuelband and SWA, while oxygen uptake was measured continuously with an Oxycon Mobile (OM) metabolic measurement system (criterion).ResultsThe Nike + Fuelband (ICC = 0.77) and SWA (ICC = 0.61) both demonstrated moderate to good validity. PAEE estimates provided by the Nike + Fuelband (246 ± 67 kcal) and SWA (238 ± 57 kcal) were not statistically different than OM (243 ± 67 kcal). Both devices also displayed similar mean absolute percent errors for PAEE estimates (Nike + Fuelband = 16 ± 13 %; SWA = 18 ± 18 %). Test-retest reliability for PAEE indicated good stability for Nike + Fuelband (ICC = 0.96) and SWA (ICC = 0.90).ConclusionThe Nike + Fuelband provided valid and reliable estimates of PAEE, that are similar to the previously validated SWA, during a routine that included approximately equal amounts of sedentary/light-, moderate- and vigorous-intensity physical activity.

Influence of accelerometer type and placement on physical activity energy expenditure prediction in manual wheelchair users.

PurposeTo assess the validity of two accelerometer devices, at two different anatomical locations, for the prediction of physical activity energy expenditure (PAEE) in manual wheelchair users (MWUs).MethodsSeventeen MWUs (36 ± 10 yrs, 72 ± 11 kg) completed ten activities; resting, folding clothes, propulsion on a 1% gradient (3,4,5,6 and 7 km·hr-1) and propulsion at 4km·hr-1 (with an additional 8% body mass, 2% and 3% gradient) on a motorised wheelchair treadmill. GT3X+ and GENEActiv accelerometers were worn on the right wrist (W) and upper arm (UA). Linear regression analysis was conducted between outputs from each accelerometer and criterion PAEE, measured using indirect calorimetry. Subsequent error statistics were calculated for the derived regression equations for all four device/location combinations, using a leave-one-out cross-validation analysis.ResultsAccelerometer outputs at each anatomical location were significantly (p < .01) associated with PAEE (GT3X+-UA; r = 0.68 and GT3X+-W; r = 0.82. GENEActiv-UA; r = 0.87 and GENEActiv-W; r = 0.88). Mean ± SD PAEE estimation errors for all activities combined were 15 ± 45%, 14 ± 50%, 3 ± 25% and 4 ± 26% for GT3X+-UA, GT3X+-W, GENEActiv-UA and GENEActiv-W, respectively. Absolute PAEE estimation errors for devices varied, 19 to 66% for GT3X+-UA, 17 to 122% for GT3X+-W, 15 to 26% for GENEActiv-UA and from 17.0 to 32% for the GENEActiv-W.ConclusionThe results indicate that the GENEActiv device worn on either the upper arm or wrist provides the most valid prediction of PAEE in MWUs. Variation in error statistics between the two devices is a result of inherent differences in internal components, on-board filtering processes and outputs of each device.

Validation of Three Physical Activity Monitors for Assessment of Energy Expenditure in Older Women

Doubly labelled water (DLW) is considered the criterion method for assessment of total energy expenditure (TEE) and physical activity energy expenditure (PAEE) under free-living conditions; however, it is costly and requires significant technical expertise. Body-worn physical activity monitors are a good proxy for estimating TEE and PAEE, but their accuracy compared to DLW remains uncertain in older women. PURPOSE: This study sought to i) validate three physical activity (PA) monitors for TEE assessment in free-living older women using DLW as the criterion method and ii) determine the accuracy of the PAEE estimates obtained by these monitors. METHODS: Thirty three women (age 60.7 ± 7.1 y; mean BMI 26.9 ± 5.0 kg·m-2) were recruited for this study. TEE and PAEE were measured over a 14-day period using a standardised protocol for DLW (2H2O and H218O). Women also wore SenseWear® armband (SAB), ActiHeart™ (AH) and ActiGraph GT3X (AG) PA monitors continuously, and at the same time, during their waking hours over 7 d, except for water-based activities such as bathing or swimming. RESULTS: Paired t-tests revealed mean differences of -163 ± 307 kcal·d-1 between TEEDLW and TEESAB; -134 ± 279 kcal·d-1 between TEEDLW and TEEAH; and -143 ± 242 kcal·d-1 between TEEDLW and TEEAG (all p<0.05). Measurements from PA monitors underestimated TEE derived from the criterion DLW by 6%-7%. The agreement between the criterion and PA devices for TEE demonstrated concordance (ρc) from 0.51 to 0.75. TEEDLW was strongly correlated with TEEAG (r=0.80; p<0.05). PAEE derived from PA monitors significantly underestimated the criterion PAEE by 18%-68%, with mean differences of -332 ± 333 kcal·d-1 between PAEEDLW and PAEESAB, -124 ± 250 kcal·d-1 between PAEEDLW and PAEEAH and -130 ± 217 kcal·d-1 between PAEEDLW and PAEEAG (all p<0.05). There was a moderate correlation between PAEEDLW and PAEEAG (r=0.61; p<0.05), but ρc was non-existent to moderate, ranging from 0.00 to 0.53. CONCLUSION: There were good levels of agreement between body-worn physical activity monitors and a criterion measurement of TEE in older women under free-living conditions. Physical activity monitors consistently underestimated daily levels of energy expenditure, although agreement with a criterion method was better for estimating TEE, than for PAEE. More work is needed to improve the ability of these PA monitors to accurately measure energy expenditure during daily physical activities.

Accuracy of a combined heart rate and motion sensor for assessing energy expenditure in free-living adults during a double-blind crossover caffeine trial using doubly labeled water as the reference method.

A combined heart rate (HR) and motion sensor (Actiheart) has been proposed as an accurate method for assessing total energy expenditure (TEE) and physical activity energy expenditure (PAEE). However, the extent to which factors such as caffeine may affect the accuracy by which the estimated HR-related PAEE contribution will affect TEE and PAEE estimates is unknown. Therefore, we examined the validity of Actiheart in estimating TEE and PAEE in free-living adults under a caffeine trial compared with doubly labeled water (DLW) as reference criterion. Using a double-blind crossover trial (Clinicaltrials.gov ID: #NCT01477294) with two conditions (4-day each with a 3-day-washout period), randomly ordered as caffeine (5 mg/kg per day) and placebo (malt-dextrine) intake, TEE was measured by DLW in 17 physically active men (20-38 years) who were non-caffeine users. In each condition, resting energy expenditure (REE) was assessed by indirect calorimetry and PAEE was calculated as (TEE-(REE+0.1 TEE)). Simultaneously, PAEE and TEE were estimated by Actiheart using an individual calibration (ACC+HRstep). Under caffeine, ACC+HRstep explained 76 and 64% of TEE and PAEE from DLW, respectively; corresponding results for the placebo condition were 82 and 66%. No mean bias was found between ACC+HRstep and DLW for TEE (caffeine:-468 kJ per day; placebo:-407 kJ per day), although PAEE was slightly underestimated (caffeine:-856 kJ per day; placebo:-1147 kJ per day). Similar limits of agreement were observed in both conditions ranging from -2066 to 3002 and from -3488 to 1776 kJ per day for TEE and PAEE, respectively. Regardless of caffeine intake, the combined HR and motion sensor is valid for estimating free-living energy expenditure in a group of healthy men but is less accurate for an individual assessment.

Comparison of the EPIC Physical Activity Questionnaire with Combined Heart Rate and Movement Sensing in a Nationally Representative Sample of Older British Adults

ObjectivesTo compare physical activity (PA) subcomponents from EPIC Physical Activity Questionnaire (EPAQ2) and combined heart rate and movement sensing in older adults.MethodsParticipants aged 60–64y from the MRC National Survey of Health and Development in Great Britain completed EPAQ2, which assesses self-report PA in 4 domains (leisure time, occupation, transportation and domestic life) during the past year and wore a combined sensor for 5 consecutive days. Estimates of PA energy expenditure (PAEE), sedentary behaviour, light (LPA) and moderate-to-vigorous PA (MVPA) were obtained from EPAQ2 and combined sensing and compared. Complete data were available in 1689 participants (52% women).ResultsEPAQ2 estimates of PAEE and MVPA were higher than objective estimates and sedentary time and LPA estimates were lower [bias (95% limits of agreement) in men and women were 32.3 (−61.5 to 122.6) and 29.0 (−39.2 to 94.6) kJ/kg/day for PAEE; −4.6 (−10.6 to 1.3) and −6.0 (−10.9 to −1.0) h/day for sedentary time; −171.8 (−454.5 to 110.8) and −60.4 (−367.5 to 246.6) min/day for LPA; 91.1 (−159.5 to 341.8) and 55.4 (−117.2 to 228.0) min/day for MVPA]. There were significant positive correlations between all self-reported and objectively assessed PA subcomponents (rho = 0.12 to 0.36); the strongest were observed for MVPA (rho = 0.30 men; rho = 0.36 women) and PAEE (rho = 0.26 men; rho = 0.25 women).ConclusionEPAQ2 produces higher estimates of PAEE and MVPA and lower estimates of sedentary and LPA than objective assessment. However, both methodologies rank individuals similarly, suggesting that EPAQ2 may be used in etiological studies in this population.

Comparison of methods to assess energy expenditure and physical activity in people with spinal cord injury

ObjectiveTo compare different methods of assessing energy expenditure (EE) and physical activity (PA) in people with spinal cord injury (SCI) under community-dwelling conditions.MethodsA reference standard encompassing the doubly labelled water (DLW) technique, heart rate monitoring (FLEX-HR), a multi-sensor armband (SenseWear Armband (SWA)), and two PA recall questionnaires were employed in 14 people with SCI to estimate EE and leisure-time PA.ResultsMean total daily energy expenditure (TDEE) assessed by DLW, FLEX-HR, and SWA were 9817 ± 2491 kJ/day, 8498 ± 1516 kJ/day, and 11414 ± 3242 kJ/day, respectively. Physical activity energy expenditure (PAEE) quantified by DLW was 2841 ± 1626 kJ/day, 2935 ± 1732 kJ/day estimated from FLEX-HR, and 2773 ± 2966 kJ/day derived from SWA. After converting the PA recall questionnaire data to EE in kJ/day, PAEE for the Physical Activity Recall Assessment for People with Spinal Cord Injury (PARA-SCI) was 2339 ± 1171 kJ/day and for Physical Activity Scale for Individuals with Physical Disabilities (PASIPD) 749 ± 1026 kJ/day. DLW-quantified PAEE was moderately associated with PARA-SCI (R2 = 0.62, P < 0.05), but not with the other estimates of PAEE (R2 ranged between 0.13 and 0.30, P > 0.05).ConclusionOur findings revealed that the PARA-SCI recall questionnaire was the best estimate of PAEE compared to the reference standard DLW approach. Although the between-method variability for SWA, FLEX-HR, and PASIPD-derived PAEE was small, there was a weak association between these methods and the criterion DLW technique. The best estimate of DLW-quantified TDEE was by FLEX-HR. SWA significantly overestimated TDEE in this population.

Statistical Considerations in the Analysis of Accelerometry-Based Activity Monitor Data

We review and discuss three statistical aspects of accelerometer-based estimates of physical activity energy expenditure (PAEE):1) the nature of the relationship between accelerometer output and PAEE;2) statistical aspects of calibration studies; and 3) two specialized statistical methods that are applicable to the problem of measurement error modeling and missing data methods.We call for a continuing development of statistical methods that use more characteristics of the accelerometer signal to estimate PAEE, advocate the use of bias and SE statistics and inclusion of cross-validation in accelerometer research designs, and encourage more efforts to understand systematic and random errors in accelerometer-based estimates of PAEE.

Comparative Validity of Physical Activity Measures in Older Adults

To compare the validity of various physical activity measures with doubly labeled water (DLW)-measured physical activity energy expenditure (PAEE) in free-living older adults. Fifty-six adults aged ≥65 yr wore three activity monitors (New Lifestyles pedometer, ActiGraph accelerometer, and a SenseWear (SW) armband) during a 10-d free-living period and completed three different surveys (Yale Physical Activity Survey (YPAS), Community Health Activities Model Program for Seniors (CHAMPS), and a modified Physical Activity Scale for the Elderly (modPASE)). Total energy expenditure was measured using DLW, resting metabolic rate was measured with indirect calorimetry, the thermic effect of food was estimated, and from these, estimates of PAEE were calculated. The degree of linear association between the various measures and PAEE was assessed, as were differences in group PAEE, when estimable by a given measure. All three monitors were significantly correlated with PAEE (r=0.48-0.60, P<0.001). Of the questionnaires, only CHAMPS was significantly correlated with PAEE (r=0.28, P=0.04). Statistical comparison of the correlations suggested that the monitors were superior to YPAS and modPASE. Mean squared errors for all correlations were high, and the median PAEE from the different tools was significantly different from DLW for all but the YPAS and regression-estimated PAEE from the ActiGraph. Objective devices more appropriately rank PAEE than self-reported instruments in older adults, but absolute estimates of PAEE are not accurate. Given the cost differential and ease of use, pedometers seem most useful in this population when ranking by physical activity level is adequate.

Using Wearable Activity Type Detection to Improve Physical Activity Energy Expenditure Estimation.

Accurate, real-time measurement of energy expended during everyday activities would enable development of novel health monitoring and wellness technologies. A technique using three miniature wearable accelerometers is presented that improves upon state-of-the-art energy expenditure (EE) estimation. On a dataset acquired from 24 subjects performing gym and household activities, we demonstrate how knowledge of activity type, which can be automatically inferred from the accelerometer data, can improve EE estimates by more than 15% when compared to the best estimates from other methods.

Integration of Physiological and Accelerometer Data to Improve Physical Activity Assessment

Accurate measurement of physical activity (PA) is a prerequisite to determine dose-response relationships between activity and health. The combination of HR and accelerometers (ACC) holds promise for improving the accuracy of PA assessment, but it is unclear how currently proposed modeling techniques compare and to what extent different levels of individual calibration (IC) of HR influence monitoring accuracy. A total of 10 men and women (25.8 +/- 3.4 yr, 1.70 +/- 0.1 m, 71.7 +/- 11.8 kg, 24.4 +/- 5.0 kg.m-2) were recruited for this study, in which IC of HR to PA energy expenditure (PAEE) during both arm crank and treadmill activity were available. Participants completed 6 h of free-living activity, during which PAEE (obtained with indirect calorimetry), HR, hip ACC, arm ACC, and leg ACC were collected. PAEE was then modeled from two different methods of combining HR and ACC (arm-leg HR+M and branched model), both with IC and group-level calibration (GC) of HR, and also from hip ACC estimates alone. Estimates of PAEE were compared with criterion values for PAEE. Combined estimates of PAEE from the arm-leg HR+M and the branched model were similar when IC was used (R2 = 0.81, SEE = 0.55 METs and R2 = 0.75, SEE = 0.61 METs, respectively). When using GC, all estimates of PAEE had larger error, but the performance of the branched model suffered less than the arm-leg HR+M model (R2 = 0.75, SEE = 0.67 METs and R2 = 0.67, SEE = 0.88 METs, respectively). Both combination modeling techniques were more precise than single-measure hip ACC estimates (R2 = 0.41, SEE = 0.96 METs). The combination of HR and ACC improves the accuracy of PAEE estimates and could be applied in large-scale epidemiological studies.

Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure.

The combination of heart rate (HR) monitoring and movement registration may improve measurement precision of physical activity energy expenditure (PAEE). Previous attempts have used either regression methods, which do not take full advantage of synchronized data, or have not used movement data quantitatively. The objective of the study was to assess the precision of branched model estimates of PAEE by utilizing either individual calibration (IC) of HR and accelerometry or corresponding mean group calibration (GC) equations. In 12 men (20.6-25.2 kg/m2), IC and GC equations for physical activity intensity (PAI) were derived during treadmill walking and running for both HR (Polar) and hipacceleration [Computer Science and Applications (CSA)]. HR and CSA were recorded minute by minute during 22 h of whole body calorimetry and converted into PAI in four different weightings (P1-4) of the HR vs. the CSA (1-P1-4) relationships: if CSA > x, we used the P1 weighting if HR > y, otherwise P2. Similarly, if CSA < or = x, we used P3 if HR > z, otherwise P4. PAEE was calculated for a 12.5-h nonsleeping period as the time integral of PAI. A priori, we assumed P1 = 1, P2 = P3 = 0.5, P4 = 0, x = 5 counts/min, y = walking/running transition HR, and z = flex HR. These parameters were also estimated post hoc. Means +/- SD estimation errors of a priori models were -4.4 +/- 29 and 3.5 +/- 20% for IC and GC, respectively. Corresponding post hoc model errors were -1.5 +/- 13 and 0.1 +/- 9.8%, respectively. All branched models had lower errors (P < or = 0.035) than single-measure estimates of CSA (less than or equal to -45%) and HR (> or =39%), as well as their nonbranched combination (> or =25.7%). In conclusion, combining HR and CSA by branched modeling improves estimates of PAEE. IC may be less crucial with this modeling technique.