Validity of the simultaneous heart rate-motion sensor technique for measuring energy expenditure

Raw Photoplethysmography as an Enhancement for Research-Grade Wearable Activity Monitors

Wearable monitors continue to play a critical role in scientific assessments of physical activity. Recently, research-grade monitors have begun providing raw data from photoplethysmography (PPG) alongside standard raw data from inertial sensors (accelerometers and gyroscopes). Raw PPG enables granular and transparent estimation of cardiovascular parameters such as heart rate, thus presenting a valuable alternative to standard PPG methodologies (most of which rely on consumer-grade monitors that provide only coarse output from proprietary algorithms). The implications for physical activity assessment are tremendous, since it is now feasible to monitor granular and concurrent trends in both movement and cardiovascular physiology using a single noninvasive device. However, new users must also be aware of challenges and limitations that accompany the use of raw PPG data. This viewpoint paper therefore orients new users to the opportunities and challenges of raw PPG data by presenting its mechanics, pitfalls, and availability, as well as its parallels and synergies with inertial sensors. This includes discussion of specific applications to the prediction of energy expenditure, activity type, and 24-hour movement behaviors, with an emphasis on areas in which raw PPG data may help resolve known issues with inertial sensing (eg, measurement during cycling activities). We also discuss how the impact of raw PPG data can be maximized through the use of open-source tools when developing and disseminating new methods, similar to current standards for raw accelerometer and gyroscope data. Collectively, our comments show the strong potential of raw PPG data to enhance the use of research-grade wearable activity monitors in science over the coming years.

Utility of wearable physical activity monitors in cardiovascular disease: a systematic review of 11 464 patients and recommendations for optimal use

Aims

Physical activity (PA) plays an important role in primary and secondary prevention of cardiovascular disease (CVD), functioning as a marker of disease progression and response to therapy. Real-world measurement of habitual PA is now possible through wearable activity monitors, however, their use in cardiovascular patients is not well described.

Methods and results

We performed a systematic review to summarize how wearable activity monitors have been used to measure PA in patients with CVD, with 11 464 patients included across 108 studies. Activity monitors were primarily used in the setting of cardiac rehabilitation (46, 43%). Most often, triaxial accelerometers (70, 65%) were instructed to be worn at the hip (58, 54%) for 7 days (n = 54, 50%). Thirty-nine different activity monitors were used, with a range of accelerometer specific settings for collection and reporting of activity data. Activity was reported most commonly as time spent in metabolic equivalent-defined activity levels (49, 45%), while non-wear time was defined in just 16 (15%) studies.

Conclusion

The collecting, processing, and reporting of accelerometer-related outcomes were highly heterogeneous. Most validation studies are limited to healthy young adults, while the paucity of methodological information disclosed renders interpretation of results and cross-study comparison challenging. While accelerometers are promising tools to measure real-world PA, we highlight current challenges facing their use in elderly multimorbid cardiology patients. We suggest recommendations to guide investigators using these devices in cardiovascular research. Future work is required to determine optimal methodology and consensus-based development of meaningful outcomes using raw acceleration data.

Cross-Sectional Study Using Virtual Reality to Measure Cognition

Dual-task research is limited in its transferability to authentic contexts because laboratory conditions do not replicate real-world physical activity and decision-making scenarios. Creating valid, reliable methodologies to assess physiological and behavioral responses under varying physical and cognitive demands using virtual reality (VR) environment addresses this limitation. This study determined the feasibility of using VR to investigate the effects of dual-tasking on healthy young adults' cognitive performance. Three dual-tasking conditions (i.e., standing, preferred-paced walking, and fast-paced walking, each with blocked congruent and incongruent tasks) were developed. Using a within-subjects, randomized design, thirty-two young adults (17 female, mean age = 21.03 ± 2.86) were randomly assigned to a starting condition but experienced all three conditions. Physiological responses of heart rate (HR) and accelerometry data measured energy expenditure as the physical demand. Behavioral responses of reaction time and error rate quantified cognitive performance. Results indicated that (a) each condition verified independent physiological and behavioral responses; (b) reaction time and error rate during preferred walking or fast-paced walking dual-tasking conditions was significantly lower than standing condition; and surprisingly, (c) congruent tasks showed lower reaction time than the incongruent tasks. These findings suggest that it is feasible to use VR to assess the effects of dual-task conditions. Specifically, walking can optimize the motor-cognitive dual-task performance, compared to standing. These findings may be attributed to the dose-response effects of exercise intensity. Future studies should incorporate advanced technology such as the VR exercise.

Estimation of energy consumed by middle-aged recreational marathoners during a marathon using accelerometry-based devices

As long-distance races have substantially increased in popularity over the last few years, the improvement of training programs has become a matter of concern to runners, coaches and health professionals. Triaxial accelerometers have been proposed as a one of the most accurate tools to evaluate physical activity during free-living conditions. In this study, eighty-eight recreational marathon runners, aged 30–45 years, completed a marathon wearing a GENEActiv accelerometer on their non-dominant wrist. Energy consumed by each runner during the marathon was estimated based on both running speed and accelerometer output data, by applying the previously established GENEActiv cut-points for discriminating the six relative-intensity activity levels. Since accelerometry allowed to perform an individualized estimation of energy consumption, higher interpersonal differences in the number of calories consumed by a runner were observed after applying the accelerometry-based approach as compared to the speed-based method. Therefore, pacing analyses should include information of effort intensity distribution in order to adjust race pacing appropriately to achieve the marathon goal time. Several biomechanical and physiological parameters (maximum oxygen uptake, energy cost of running and running economy) were also inferred from accelerometer output data, which is of great value for coaches and doctors.

Prediction of energy expenditure during activities of daily living by a wearable set of inertial sensors

Physical inactivity is responsible for 7-10% of all premature deaths worldwide. Thus, valid, reliable and unobtrusive methods for monitoring activities of daily living (ADL) to predict total energy expenditure (TEE) is desired. Multiple methods exist to quantify TEE, but microelectromechanical systems (MEMSs) are the only method, which has shown promising results and are applicable for long-term monitoring in the field. However, no perfect method exists for predicting TEE on a daily basis. The present study evaluates TEE estimation based on a MEMS (Xsens Link system) taking gender and heart rate into account. Fifteen individuals performed seven ADL wearing the Xsens Link system, a heart rate belt and an oxygen mask. Multiple linear regression models were established for sedentary and dynamic activities and evaluated by leave-one-out cross-validation and compared with indirect calorimetry. The linear regression model showed better prediction for dynamic activities (adjusted R² 0.95±0.16) compared to sedentary activities (adjusted R² 0.61±0.19). The root-mean-square error for the TEE estimation ranged between 0.02 and 0.08 kJ/min/kg for the sedentary and dynamic models, respectively. The study showed a viable approach to predict TEE in ADL compared to previously published results. Further studies are warranted to reduce the number of sensors in the estimation of TEE.

Evaluation of physiological workload assessment methods using heart rate and accelerometry for a smart wearable system

Work metabolism (WM) can be accurately estimated by oxygen consumption (VO₂), which is commonly assessed by heart rate (HR) in field studies. However, the VO₂-HR relationship is influenced by individual capacity and activity characteristics. The purpose of this study was to evaluate three models for estimating WM compared with indirect calorimetry, during simulated work activities. The techniques were: the HR-Flex model; HR branched model, combining HR with hip-worn accelerometers (ACC); and HR + arm-leg ACC model, combining HR with wrist- and thigh-worn ACC. Twelve participants performed five simulated work activities and three submaximal tests. The HR + arm-leg ACC model had the overall best performance with limits of agreement (LoA) of -3.94 and 2.00 mL/min/kg, while the HR-Flex model had -5.01 and 5.36 mL/min/kg and the branched model, -6.71 and 1.52 mL/min/kg. In conclusion, the HR + arm-leg ACC model should, when feasible, be preferred in wearable systems for WM estimation. Practitioner Summary: Work with high energy demand can impair employees' health and life quality. Three models were evaluated for estimating work metabolism during simulated tasks. The model combining heart rate, wrist- and thigh-worn accelerometers showed the best accuracy. This is, when feasible, suggested for wearable systems to assess work metabolism.

Fusion of Heart Rate, Respiration and Motion Measurements from a Wearable Sensor System to Enhance Energy Expenditure Estimation

This paper presents a new method that integrates heart rate, respiration, and motion information obtained from a wearable sensor system to estimate energy expenditure. The system measures electrocardiography, impedance pneumography, and acceleration from upper and lower limbs. A multilayer perceptron neural network model was developed, evaluated, and compared to two existing methods, with data from 11 subjects (mean age, 27 years, range, 21⁻65 years) who performed a 3-h protocol including submaximal tests, simulated work tasks, and periods of rest. Oxygen uptake was measured with an indirect calorimeter as a reference, with a time resolution of 15 s. When compared to the reference, the new model showed a lower mean absolute error (MAE = 1.65 mL/kg/min, R² = 0.92) than the two existing methods, i.e., the flex-HR method (MAE = 2.83 mL/kg/min, R² = 0.75), which uses only heart rate, and arm-leg HR+M method (MAE = 2.12 mL/kg/min, R² = 0.86), which uses heart rate and motion information. As indicated, this new model may, in combination with a wearable system, be useful in occupational and general health applications.

A systematic literature review of reviews on techniques for physical activity measurement in adults: a DEDIPAC study

The links between increased participation in Physical Activity (PA) and improvements in health are well established. As this body of evidence has grown, so too has the search for measures of PA with high levels of methodological effectiveness (i.e. validity, reliability and responsiveness to change). The aim of this “review of reviews” was to provide a comprehensive overview of the methodological effectiveness of currently employed measures of PA, to aid researchers in their selection of an appropriate tool. A total of 63 review articles were included in this review, and the original articles cited by these reviews were included in order to extract detailed information on methodological effectiveness.

Self-report measures of PA have been most frequently examined for methodological effectiveness, with highly variable findings identified across a broad range of behaviours. The evidence-base for the methodological effectiveness of objective monitors, particularly accelerometers/activity monitors, is increasing, with lower levels of variability observed for validity and reliability when compared to subjective measures. Unfortunately, responsiveness to change across all measures and behaviours remains under-researched, with limited information available.

Other criteria beyond methodological effectiveness often influence tool selection, including cost and feasibility. However, researchers must be aware of the methodological effectiveness of any measure selected for use when examining PA. Although no “perfect” tool for the examination of PA in adults exists, it is suggested that researchers aim to incorporate appropriate objective measures, specific to the behaviours of interests, when examining PA in free-living environments.

Measurement of physical activity in obese persons: how and why? A review

[Purpose] Overweight and obesity are major risk factors for poor health, especially in children. Reduced physical activity, prompted by a sedentary lifestyle, is a major contributor. Hence, it is important to assess physical activity using standardized methods in public health to identify the risks associated with obesity. There have been no recent reports comparing such modalities for use by clinicians and researchers. In this article, some of these methods for use in the assessment of physical activity are reviewed, and their advantages and disadvantages are described. [Subjects and Methods] Electronic databases including PubMed, Medline, and Google Scholar were searched for literature, using key words Obesity, Physical activity, and Physical Behavior Monitoring. [Results] With advances in technology, various novel methods have been developed to assess physical behavior, but conventional methods are still relevant and easy to administer. [Conclusion] There are various measurement options available. Researchers may choose devices providing more accurate measurements, while clinicians may prefer portability and affordability for patients.

Estimation of Free-Living Energy Expenditure by Heart Rate and Movement Sensing: A Doubly-Labelled Water Study

Background

Accurate assessment of energy expenditure (EE) is important for the study of energy balance and metabolic disorders. Combined heart rate (HR) and acceleration (ACC) sensing may increase precision of physical activity EE (PAEE) which is the most variable component of total EE (TEE).

Objective

To evaluate estimates of EE using ACC and HR data with or without individual calibration against doubly-labelled water (DLW) estimates of EE.

Design

23 women and 23 men (22–55 yrs, 48–104 kg, 8–46%body fat) underwent 45-min resting EE (REE) measurement and completed a 20-min treadmill test, an 8-min step test, and a 3-min walk test for individual calibration. ACC and HR were monitored and TEE measured over 14 days using DLW. Diet-induced thermogenesis (DIT) was calculated from food-frequency questionnaire. PAEE (TEE ÷ REE ÷ DIT) and TEE were compared to estimates from ACC and HR using bias, root mean square error (RMSE), and correlation statistics.

Results

Mean(SD) measured PAEE and TEE were 66(25) kJ·day^-1·kg^-1, and 12(2.6) MJ·day^-1, respectively. Estimated PAEE from ACC was 54(15) kJ·day^-1·kg^-1 (p<0.001), with RMSE 24 kJ·day^-1·kg^-1 and correlation r = 0.52. PAEE estimated from HR and ACC+HR with treadmill calibration were 67(42) and 69(25) kJ·day^-1·kg^-1 (bias non-significant), with RMSE 34 and 20 kJ·day^-1·kg^-1 and correlations r = 0.58 and r = 0.67, respectively. Similar results were obtained with step-calibrated and walk-calibrated models, whereas non-calibrated models were less precise (RMSE: 37 and 24 kJ·day^-1·kg^-1, r = 0.40 and r = 0.55). TEE models also had high validity, with biases <5%, and correlations r = 0.71 (ACC), r = 0.66–0.76 (HR), and r = 0.76–0.83 (ACC+HR).

Conclusions

Both accelerometry and heart rate may be used to estimate EE in adult European men and women, with improved precision if combined and if heart rate is individually calibrated.

Energy consumption, body composition and physical activity levels in 11- to 13-year-old Spanish children

BACKGROUND/AIMS

The prevalence of overweight and obesity in childhood is increasing markedly. The aim of this study was to evaluate the relationship between physical activity, energy consumption and weight status in a cohort of Spanish children.

METHODS

A total of 137 children (11-13 years old) participated in the study voluntarily (with paternal consent). Children were classified into 3 groups according to their physical activity, i.e. sedentary, active and sporty groups. Body composition measures, physical fitness and total caloric and macronutrient intake were determined.

RESULTS

Weight, body mass index, waist circumference, skinfold thickness measures and body fat percentage of children were lower in the active children, whereas body water content increased with activity. Nutritional habits were similar in the 3 studied groups. No significant differences in total energy intake or percentage of carbohydrates, fat and proteins were found. All parameters related to caloric expenditure were higher in children carrying out more physical activity.

CONCLUSIONS

Children with higher levels of physical activity presented more favorable anthropometric profiles, but there were no differences in respect to their dietary habits. An increase in weekly energy expenditure through physical activity outside school seems essential to prevent overweight and the risk of childhood obesity.

Computational methods for estimating energy expenditure in human physical activities

Accurate and reliable methods for assessing human physical activity (PA) energy expenditure (PAEE) are informative and essential for understanding individual behaviors and quantifying the effect of PA on disease, for PA surveillance, and for examining determinants of PA in different populations. This article reviews recent advances in the estimation of PAEE in three interrelated areas: 1) types of sensors worn by human subjects, 2) features extracted from the measured sensor signals, and 3) modeling techniques to estimate the PAEE using these features. The review illustrates three directions in the PAEE studies and provides recommendations for future research, with the aim to produce valid, reliable, and accurate assessment of PAEE from wearable sensors.

Assessing physical activity using wearable monitors: measures of physical activity

BACKGROUND

Physical activity may be defined broadly as "all bodily actions produced by the contraction of skeletal muscle that increase energy expenditure above basal level." Physical activity is a complex construct that can be classified into major categories qualitatively, quantitatively, or contextually. The quantitative assessment of physical activity using wearable monitors is grounded in the measurement of energy expenditure. Six main categories of wearable monitors are currently available to investigators: pedometers, load transducers/foot-contact monitors, accelerometers, HR monitors, combined accelerometer and HR monitors, and multiple sensor systems.

BEST PRACTICES

Currently available monitors are capable of measuring total physical activity as well as components of physical activity that play important roles in human health. The selection of wearable monitors for measuring physical activity will depend on the physical activity component of interest, study objectives, characteristics of the target population, and study feasibility in terms of cost and logistics.

FUTURE DIRECTIONS

Future development of sensors and analytical techniques for assessing physical activity should focus on the dynamic ranges of sensors, comparability for sensor output across manufacturers, and the application of advanced modeling techniques to predict energy expenditure and classify physical activities. New approaches for qualitatively classifying physical activity should be validated using direct observation or recording. New sensors and methods for quantitatively assessing physical activity should be validated in laboratory and free-living populations using criterion methods of calorimetry or doubly labeled water.

Estimation of resistance exercise energy expenditure using triaxial accelerometry

Recently, it was demonstrated that a uniaxial accelerometer worn at the hip could estimate resistance exercise energy expenditure. As resistance exercise takes place in more than 1 plane, the use of a triaxial accelerometer may be more effective in estimating resistance exercise energy expenditure. The aims of this study were to estimate the energy cost of resistance exercise using triaxial accelerometry and to determine the optimal location for wearing triaxial accelerometers during resistance exercise. Thirty subjects (15 men and 15 women; age = 21.7 ± 1.0 years) performed a resistance exercise protocol consisting of 2 sets of 8 exercises (10RM loads). During the resistance exercise protocol, subjects wore triaxial accelerometers on the wrist, waist, and ankle; a heart rate monitor; and a portable metabolic system. Net energy expenditure was significantly correlated with vertical (r = 0.67, p < 0.001), horizontal (r = 0.43, p = 0.02), third axis (r = 0.36, p = 0.048), and sum of 3 axes (r = 0.50, p = 0.005) counts at the waist, and horizontal counts at the wrist (r = -0.40, p = 0.03). Regression analysis using fat-free mass, sex, and the sum of accelerometer counts at the waist as variables was used to develop an equation that explained 73% of the variance of resistance exercise energy expenditure. A triaxial accelerometer worn at the waist can be used to estimate resistance exercise energy expenditure but appears to offer no benefit over uniaxial accelerometry. The use of accelerometers in estimating resistance exercise energy expenditure may prove useful for individuals and athletes who participate in resistance training and are focused on maintaining a tightly regulated energy balance.

Validity of activity monitors in health and chronic disease: a systematic review

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.

Laboratory and field methods for measuring human energy expenditure

Energetics research is central to the field of human biology. Energy is an important currency for measuring adaptation, because both its acquisition and allocation for biological processes have important implications for survival and reproduction. Recent technological and methodological advances are now allowing human biologists to study variation in energy dynamics with much greater accuracy in a wide variety of ecological contexts. This article provides an overview of the methods used for measuring human energy expenditure (EE) and considers some of the important ecological and evolutionary questions that can be explored from an energetics perspective. Basic principles of calorimetry are first presented, followed by an overview of the equipment used for measuring human EE and work capacity. Methods for measuring three important dimensions of human EE-resting metabolic rate, working/exercising EE, and total EE-are then presented, highlighting key areas of ongoing research.

Accurate prediction of energy expenditure using a shoe-based activity monitor

PURPOSE

The aim of this study was to develop and validate a method for predicting energy expenditure (EE) using a footwear-based system with integrated accelerometer and pressure sensors.

METHODS

We developed a footwear-based device with an embedded accelerometer and insole pressure sensors for the prediction of EE. The data from the device can be used to perform accurate recognition of major postures and activities and to estimate EE using the acceleration, pressure, and posture/activity classification information in a branched algorithm without the need for individual calibration. We measured EE via indirect calorimetry as 16 adults (body mass index=19-39 kg·m) performed various low- to moderate-intensity activities and compared measured versus predicted EE using several models based on the acceleration and pressure signals.

RESULTS

Inclusion of pressure data resulted in better accuracy of EE prediction during static postures such as sitting and standing. The activity-based branched model that included predictors from accelerometer and pressure sensors (BACC-PS) achieved the lowest error (e.g., root mean squared error (RMSE)=0.69 METs) compared with the accelerometer-only-based branched model BACC (RMSE=0.77 METs) and nonbranched model (RMSE=0.94-0.99 METs). Comparison of EE prediction models using data from both legs versus models using data from a single leg indicates that only one shoe needs to be equipped with sensors.

CONCLUSIONS

These results suggest that foot acceleration combined with insole pressure measurement, when used in an activity-specific branched model, can accurately estimate the EE associated with common daily postures and activities. The accuracy and unobtrusiveness of a footwear-based device may make it an effective physical activity monitoring tool.

The development and evaluation of a novel Internet-based computer program to assess previous-day dietary and physical activity behaviours in adults: the Synchronised Nutrition and Activity Program for Adults (SNAPA™)

The Synchronised Nutrition and Activity Program for Adults (SNAPA™) was developed to address the need for accurate, reliable, feasible, inexpensive and low-burden methods for assessing specific dietary and physical activity behaviours in adults. Short-term test-retest reliability of SNAPA™ was assessed in forty-four adults (age 41·4 (SD17·3) years) who completed SNAPA™ twice in 1 day. Concurrent validity against direct dietary observation and combined heart rate and accelerometry was assessed in seventy-seven adults (age 34·4 (SD11·1) years). Test-retest reliability revealed no substantial systematic shifts in mean values of the outcome variables: percentage of food energy from fat (% fat), number of portions of fruit and vegetables (FV) and minutes of moderate-to-vigorous physical activity (MVPA). For lunchtime dietary intake, the mean match rate between food items reported using SNAPA™ and those observed was 81·7%, with a phantom rate of 5·6%. Pearson's correlations between SNAPA™ and the reference methods ranged from 0·27 to 0·56 for % fat, FV portions and minutes of MVPA. For % fat and FV intake, there was no fixed or proportional bias, and mean differences between the methods (SNAPA™ - reference) were 5·1% and 0 portions, respectively. For minutes of MVPA, a fixed bias of - 28 min was revealed when compared with all minutes of MVPA measured by combined heart rate and accelerometry, whereas a proportional bias (slope 1·47) was revealed when compared with minutes carried out in bouts ≥ 10 min. SNAPA™ is a promising tool for measuring specific energy balance behaviours, though further work is required to improve accuracy for physical activity behaviours.

Accuracy of optimized branched algorithms to assess activity-specific physical activity energy expenditure

PURPOSE

To assess the activity-specific accuracy achievable by branched algorithm (BA) analysis of simulated daily living physical activity energy expenditure (PAEE) within a sedentary population.

METHODS

Sedentary men (n = 8) and women (n = 8) first performed a treadmill calibration protocol, during which HR, accelerometry (ACC), and PAEE were measured in 1-min epochs. From these data, HR-PAEE and ACC-PAEE regressions were constructed and used in each of six analytic models to predict PAEE from ACC and HR data collected during a subsequent simulated daily living protocol. Criterion PAEE was measured during both protocols via indirect calorimetry. The accuracy achieved by each model was assessed by the root mean square of the difference between model-predicted daily living PAEE and the criterion daily living PAEE (expressed here as percent of mean daily living PAEE).

RESULTS

Across the range of activities, an unconstrained post hoc-optimized BA best predicted criterion PAEE. Estimates using individual calibration were generally more accurate than those using group calibration (14% vs 16% error, respectively). These analyses also performed well within each of the six daily living activities, but systematic errors appeared for several of those activities, which may be explained by an inability of the algorithm to simultaneously accommodate a heterogeneous range of activities. Analyses between mean square error by subject and activity suggest that optimization involving minimization of root mean square for total daily living PAEE is associated with decreased error between subjects but increased error between activities.

CONCLUSIONS

The performance of post hoc-optimized BA may be limited by heterogeneity in the daily living activities being performed.

Physiological and behavioural response patterns at work among hospital nurses

AIM

The aim was to determine whether hospital nurses are experiencing physiological strain at work by examining their physiological and behavioural response patterns over 12-hour shifts.

BACKGROUND

Excessive workload for nurses may lead to poor quality of care and high nursing turnover rates. Energy expenditure (EE), heart rate (HR) and work pace (WP) can be used to examine the physiological impact from the workload.

METHODS

A total of 145 nurses wore monitors for one 12-hour day shift to record HR and WP, which were used to calculate EE. Individual and work-related factors were assessed through questionnaires and work logs.

RESULTS

Energy expenditure accumulated over the 12 hours reached the EE level of 8-hour shifts in which individuals work at a moderate physical intensity level. The HR data indicated a moderate cardiac stress level throughout the shifts, despite which WP decreased after 15.00 hours. Inadequate work break and sleep, family care-giving responsibility and aging may challenge work recovery.

CONCLUSIONS

Nursing workload of 12-hour shifts has a negative physiological impact on hospital nurses.

IMPLICATIONS FOR NURSING MANAGEMENT

Nurse managers need to be aware of the physiological strain experienced by staff nurses, and focus on ensuring sufficient breaks and proper work accommodations for older nurses.

Measuring physical activity: practical approaches for program evaluation in Native American communities

Promoting physical activity is a high priority in the United States, especially for Native American populations, due to very high rates of inactivity-related chronic diseases. High-quality physical activity measures can contribute to achieving health goals. Measuring a sample of the population can identify high-risk subgroups and geographic locations that can be targeted for interventions. Outcomes of physical activity interventions should be evaluated because this is the only way to determine whether they are effective. Three types of measures are practical for use in nonresearch settings, although they still present challenges. First, self-reports are commonly used; they are low-cost but the least accurate. Second, objective monitors such as pedometers, accelerometers, and heart rate monitors can provide accurate information, but resources and expertise are needed to collect and manage data. Third, direct observation can be used to evaluate school physical education programs and assess how people are using parks and other physical activity facilities. Studies of Native American populations have used a variety of measures. Good evaluations can lead to program improvements, documenting positive results can attract funding to continue and expand programs, and communicating results can persuade other communities to adopt effective approaches. Program evaluations using quality physical activity measures can contribute to achieving the goal of improved health in Native American communities.

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.

Multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents

Advanced mathematical models have the potential to capture the complex metabolic and physiological processes that result in heat production or energy expenditure (EE). Multivariate adaptive regression splines (MARS) is a nonparametric method that estimates complex nonlinear relationships by a series of spline functions of the independent predictors. The specific aim of this study is to construct MARS models based on heart rate (HR) and accelerometer counts (AC) to accurately predict EE, and hence 24-h total EE (TEE), in children and adolescents. Secondarily, MARS models will be developed to predict awake EE, sleep EE, and activity EE also from HR and AC. MARS models were developed in 109 and validated in 61 normal-weight and overweight children (ages 5–18 yr) against the criterion method of 24-h room respiration calorimetry. Actiheart monitor was used to measure HR and AC. MARS models were based on linear combinations of 23–28 basis functions that use subject characteristics (age, sex, weight, height, minimal HR, and sitting HR), HR and AC, 1- and 2-min lag and lead values of HR and AC, and appropriate interaction terms. For the 24-h, awake, sleep, and activity EE models, mean percent errors were −2.5 ± 7.5, −2.6 ± 7.8, −0.3 ± 8.9, and −11.9 ± 17.9%, and root mean square error values were 168, 138, 40, and 122 kcal, respectively, in the validation cohort. Bland-Altman plots indicated that the predicted values were in good agreement with the observed TEE, and that there was no bias with increasing TEE. Prediction errors for 24-h TEE were not statistically associated with age, sex, weight, height, or body mass index. MARS models developed for the prediction of EE from HR monitoring and accelerometry were demonstrated to be valid in an independent cohort of children and adolescents, but require further validation in independent, free-living populations.

Influence of activity monitor location and bout duration on free-living physical activity

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

Application of cross-sectional time series modeling for the prediction of energy expenditure from heart rate and accelerometry

Accurate estimation of energy expenditure (EE) in children and adolescents is required for a better understanding of physiological, behavioral, and environmental factors affecting energy balance. Cross-sectional time series (CSTS) models, which account for correlation structure of repeated observations on the same individual, may be advantageous for prediction of EE. CSTS models for prediction of minute-by-minute EE and, hence, total EE (TEE) from heart rate (HR), physical activity (PA) measured by accelerometry, and observable subject variables were developed in 109 children and adolescents by use of Actiheart and 24-h room respiration calorimetry. CSTS models based on HR, PA, time-invariant covariates, and interactions were developed. These dynamic models involve lagged and lead values of HR and lagged values of PA for better description of the series of minute-by-minute EE. CSTS models with random intercepts and random slopes were investigated. For comparison, likelihood ratio tests were used. Log likelihood increased substantially when random slopes for HR and PA were added. The population-specific model uses HR and 1- and 2-min lagged and lead values of HR, HR(2), and PA and 1- and 2-min lagged values of PA, PA(2), age, age(2), sex, weight, height, minimum HR, sitting HR, HR x height, HR x weight, HR x age, PA x weight, and PA x sex interactions (P < 0.001). Prediction error for TEE was 0.9 +/- 10.3% (mean +/- SD). Errors were not correlated with age, weight, height, or body mass index. CSTS modeling provides a useful predictive model for EE and, hence, TEE in children and adolescents on the basis of HR and PA and other observable explanatory subject characteristics of age, sex, weight, and height.

Activity monitoring in assessing activities of daily living

Individuals who have active lifestyles appear to reap substantial benefits. It is therefore of interest to assess level of activity and to determine whether interventions are capable of altering activities of daily life. Questionnaires are often employed because of their simplicity, but objective measures are sought. Long-term assessment of energy expenditure, either through doubly labeled water analysis or through measurements of expired gases are expensive and often impractical. Activity monitors include pedometers, heart rate monitors, accelerometers and integrated multisensor systems. Rapidly advancing activity monitor technology has enabled long-term use and facilitated downloading of recordings to computers where sophisticated analysis of activity patterns can be made. Accelerometer-based systems have received the most attention. When applied to chronic obstructive pulmonary disease patients, accelerometric monitors have demonstrated low levels of activity; those using long-term oxygen and those having exacerbations are particularly inactive.

Physical activity level and health-related quality of life in the general adult population: a systematic review

OBJECTIVE

Little is known regarding health-related quality of life and its relation with physical activity level in the general population. Our primary objective was to systematically review data examining this relationship.

METHODS

We systematically searched MEDLINE, EMBASE, CINAHL, and PsycINFO for health-related quality of life and physical activity related keywords in titles, abstracts, or indexing fields.

RESULTS

From 1426 retrieved references, 55 citations were judged to require further evaluation. Fourteen studies were retained for data extraction and analysis; seven were cross-sectional studies, two were cohort studies, four were randomized controlled trials and one used a combined cross sectional and longitudinal design. Thirteen different methods of physical activity assessment were used. Most health-related quality of life instruments related to the Medical Outcome Study SF-36 questionnaire. Cross-sectional studies showed a consistently positive association between self-reported physical activity and health-related quality of life. The largest cross-sectional study reported an adjusted odds ratio of "having 14 or more unhealthy days" during the previous month to be 0.40 (95% Confidence Interval 0.36-0.45) for those meeting recommended levels of physical activity compared to inactive subjects. Cohort studies and randomized controlled trials tended to show a positive effect of physical activity on health-related quality of life, but similar to the cross-sectional studies, had methodological limitations.

CONCLUSION

Cross-sectional data showed a consistently positive association between physical activity level and health-related quality of life. Limited evidence from randomized controlled trials and cohort studies precludes a definitive statement about the nature of this association.

Accuracy of the Actiheart for the assessment of energy expenditure in adults

BACKGROUND/OBJECTIVE

The Actiheart (Mini Mitter, Sunriver, OR, USA) uses heart rate (HR) and activity data to predict activity energy expenditure (AEE). Currently, the Actiheart has only been tested during laboratory conditions. Therefore, the objective of this study was to validate the Actiheart prediction method against indirect calorimetry during a wide range of activities in a field setting.

SUBJECTS/METHODS

Forty-eight participants (age: 35+/-11.4 years) were recruited for the study. Eighteen activities were split into three routines of six activities and each routine was performed by 20 participants. During each routine, the participants wore an Actiheart and simultaneously, AEE was measured with a Cosmed K4b(2) portable metabolic system. The manufacturer's HR algorithm, activity algorithm, and combined activity and HR algorithm were used to estimate AEE.

RESULTS

The mean error (and 95% prediction intervals) for the combined activity and HR algorithm, HR algorithm, and activity algorithm versus the Cosmed K4b(2) were 0.02 kJ kg(-1) min(-1) (-0.17, 0.22 kJ kg(-1) min(-1)), -0.03 kJ kg(-1) min(-1) (-0.24, 0.18 kJ kg(-1) min(-1)), and 0.14 kJ kg(-1) min(-1) (-0.12, 0.40 kJ kg(-1) min(-1)), respectively.

CONCLUSION

The Actiheart combined activity and HR algorithm and HR algorithm provide similar estimates of AEE on both a group and individual basis.

Estimation of oxygen uptake during fast running using accelerometry and heart rate

Previous investigations have reported that accelerometer counts plateau during running at increasingly faster speeds.

PURPOSE

To assess whether biomechanical and/or device limitations cause this phenomenon and the feasibility of generating oxygen uptake (.VO2) prediction equations from the combined use of accelerometry and heart rate during walking and running.

METHODS

: Sixteen endurance-trained subjects completed two exercise tests on a treadmill. The first was a continuous incremental test to volitional exhaustion to determine ventilatory threshold and peak .VO2. The second was a discontinuous incremental exercise test while walking (3, 5, and 7 km.h(-1)) and running (8, 10, 12, 14, 16, 18, and 20 km.h(-1), or until volitional exhaustion). Subjects completed 3 min of exercise at each speed, followed by 3-5 min of recovery. Activity counts from uni- and triaxial accelerometers, heart rate, and gas exchange were measured throughout exercise.

RESULTS

All accelerometer outputs rose linearly with speed during walking. During running, uniaxial accelerometer outputs plateaued, whereas triaxial output rose linearly with speed up to and including 20 km.h(-1). Prediction of .VO2 during walking and running using heart rate (R2 = 0.42 and 0.59, respectively), accelerometer counts (R2 = 0.48-0.83 and 0.76, respectively), the combined methodologies (R2 = 0.54-0.85 and 0.80, respectively), and the combined methodologies calibrated with individual data (R2 = 0.99-1.00 and 0.99, respectively) was completed by linear regression.

CONCLUSIONS

Uni- and triaxial accelerometer outputs have a linear relationship with speed during walking. During running, uniaxial accelerometer outputs plateau because of the biomechanics of running, whereas triaxial accelerometer output has a linear relationship. The combined methodologies predict .VO2 better than either predictor alone; a subject's individually calibrated data further improves .VO2 estimation.

Noninvasive assessment of energy expenditure in children

This study establishes an affordable, simple, and noninvasive method to assess energy expenditure (EE) in children, an underrepresented group. The method is based on regression modeling, where prediction of oxygen consumption (VO(2)), a proxy of EE, was deduced from heart rate (HR) and several variables that adjusted for interindividual variability. Limb activities (arms vs. legs) and posture (sitting vs. standing) were represented in the regression as dichotomous covariates. The order of activities and intensities was randomized. Seventy-four children (aged 7-10 years), raised at sea-level (Seattle, WA), comprised the sample. Anthropometric measures were taken, and VO(2) and HR were measured for activities using the arms in sitting and standing positions (mixing and punching), as well as walking at different velocities on a treadmill. Repeated measures and least square regression estimation were used. HR, body mass, number of hours of physical activity per week (HPA), an interaction term between sitting and standing resting HR, and the two dichotomous variables, sex and limbs, were significant covariates; posture was not. Several equations were developed for various field uses. The equations were built from sea-level data, but ultimately this method could serve as a baseline for developing a similar approach in other populations, where noninvasive estimation of EE is imperative in order to gain a better understanding of children's energetic issues.

Accelerometry combined with heart rate telemetry in the assessment of total energy expenditure

The aim of the present study was: (1) to develop a new method for total energy expenditure (TEE) assessment, using accelerometry (ACC) and heart rate (HR) telemetry in combination; (2) to validate the new method against the criterion measure (DLW) and to compare with two of the most common methods, FLEX-HR and ACC alone. In the first part of the study VO(2), HR and ACC counts were measured in twenty-seven subjects during walking and running on a treadmill. Considering the advantages and disadvantages of the HR and ACC methods an analysis model was developed, using ACC at intensities of low and medium levels and HR at higher intensities. During periods of inactivity, RMR is used. A formula for determining TEE from ACC, HR and RMR was developed: TEE = 1.1x(EQ(HR) x TT(HR) + EQ(ACC1) x TT(ACC1) + EQ(ACC2) x TTACC2 + RMR x TT(RMR)). In the validation part of the study a sub-sample of eight subjects wore an accelerometer, HR was logged and TEE was measured for 14 d with the DLW method. Analysis of the Bland-Altman plots with 95 % CI indicates that there are no significant differences in TEE estimated with HR-ACC and ACC alone compared with TEE measured with DLW. It is concluded that the HR-ACC combination as well as ACC alone has potential as a method for assessment of TEE during free-living activities as compared with DLW.

Assessment of low-to-moderate intensity physical activity thermogenesis in young adults using synchronized heart rate and accelerometry with branched-equation modeling

Low-to-moderate intensity physical activity thermogenesis is a highly variable and quantitatively important component of total energy expenditure that is difficult to assess outside the laboratory. Greater precision and accuracy in the measurement of this key contributor to energy balance is a research priority. We developed a laboratory-based protocol that simulated a range of low-to-moderate intensity physical activities. We characterized the bias and random (individual) error in estimating energy expenditure using combined accelerometry and heart rate (AHR) with branched-equation modeling and a simple motion sensor (pedometer) against an indirect calorimetry criterion. Twenty young adult subjects performed a 2-h laboratory-based protocol, simulating 6 low-to-moderate intensity physical activities interspersed with periods of rest. The physical activity level during the laboratory-based protocol reflected an energy expenditure toward the lower end of the active category. We found that AHR-derived energy expenditure showed no evidence of substantial fixed or proportional bias (mean bias 6%), whereas pedometer-derived energy expenditure showed both fixed and proportional bias (bias at minimum, mean, and maximum energy expenditure +11, -20, and -36%, respectively). It appears that AHR provides an accurate estimate of criterion energy expenditure whereas a simple motion sensor (pedometer) does not. It is noteworthy that AHR provides quantitative information about the nature and patterns of physical activity, such as the amount of time and/or energy spent engaged in physical activity above critical health-related thresholds.

Individual Calibration for Estimating Free-Living Walking Speed Using the MTI Monitor

PURPOSE

This study was conducted to devise a new individual calibration method to enhance MTI accelerometer estimation of free-living level walking speed.

METHOD

Five female and five male middle-aged adults walked 400 m at 3.5, 4.5, and 5.5 km x h(-1), and 800 m at 6.5 km x h(-1) on an outdoor track, following a continuous protocol. Lap speed was controlled by a global positioning system (GPS) monitor. MTI counts-to-speed calibration equations were derived for each trial, for each subject for four such trials with each of four MTI, for each subject for the average MTI, and for the pooled data. Standard errors of the estimate (SEE) with and without individual calibration were compared. To assess accuracy of prediction of free-living walking speed, subjects also completed a self-paced, "brisk" 3-km walk wearing one of the four MTI, and differences between actual and predicted walking speed with and without individual calibration were examined.

RESULTS

Correlations between MTI counts and walking speed were 0.90 without individual calibration, 0.98 with individual calibration for the average MTI, and 0.99 with individual calibration for a specific MTI. The SEE (mean +/- SD) was 0.58 +/- 0.30 km x h(-1) without individual calibration, 0.19 +/- 0.09 km x h(-1) with individual calibration for the average MTI monitor, and 0.16 +/- 0.08 km x h(-1) with individual calibration for a specific MTI monitor. The difference between actual and predicted walking speed on the "brisk" 3-km walk was 0.06 +/- 0.25 km x h(-1) using individual calibration and 0.28 +/- 0.63 km x h(-1) without individual calibration (for specific accelerometers).

CONCLUSION

MTI accuracy in predicting walking speed without individual calibration might be sufficient for population-based studies but not for intervention trials. This individual calibration method will substantially increase precision of walking speed predicted from MTI counts.

Alterations of blood pressure in type 1 diabetic children and adolescents

The aim of this study was to assess the association between metabolic control, microalbuminuria, and diabetic nephropathy with ambulatory blood pressure monitoring (ABPM) in normotensive individuals with type 1 diabetes mellitus (DM). ABPM was undertaken in 68 normotensive type 1 diabetic patients with a mean age of 14.4+/-4.2 years. Microalbuminuria was diagnosed on the basis of a urinary albumin excretion rate grater than 20 microg/min in two of the three 24-h urine collections. Hypertension (HT) frequency was greater in the microalbuminuric patients than normoalbuminuric patients (54 vs 17.54%, p=0.05) with ABPM. Microalbuminuric patients had a higher diastolic pressure burden than normoalbuminuric patients. There were no differences in systolic and diastolic dips between the two groups. Diastolic pressure loads in all periods showed a significant correlation with duration of diabetes, mean HbA1c from the onset of diabetes, and level of microalbuminuria. Nocturnal dipping was reduced in 41.2% of the patients. In the normoalbuminuric group 41.1% and in the microalbuminuric group 63.6% were nondippers. Our data demonstrate higher 24-h and daytime diastolic blood pressure load and loss of nocturnal dip in type 1 diabetic adolescents and children. High diastolic blood pressure burden in diabetic patients could represent a risk for nephropathy.

Calibration of accelerometer output for adults

This paper reviews the collective experience of monitor calibration studies in adults and seeks to answer the following questions: What has been done? What have we learned? What could be done to further enhance the comparability of results from future calibration research? Calibration studies in adults have typically used oxygen consumption as a criterion measure, similar types of source activities, and linear regression to obtain prediction equations that calibrate the activity counts to measured activity intensity levels. However, the methodological diversity of these studies has produced a great deal of variation in the resulting prediction equations and cut points, even when using the same monitor. Thus, data obtained from a relatively robust activity monitoring technology that captures many dynamic physical activities reasonably well have been splintered by the calibration process into a wide range of summary measures that are much less comparable than they could otherwise be. This heterogeneity in calibration results reduces our ability to interpret data obtained from accelerometers between different research groups, across the life span, between populations, and probably between the different monitor types. This report reviews and critiques methods typically used for developing calibration equations and determining activity count cut points for identifying specific intensities of PA among adults, and it highlights the need for flexible research methods that can enhance the comparability of results from future calibration studies.

Integration of physiological and accelerometer data to improve physical activity assessment

PURPOSE

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.

METHODS

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.

RESULTS

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).

CONCLUSION

The combination of HR and ACC improves the accuracy of PAEE estimates and could be applied in large-scale epidemiological studies.

Physical activity measurement in persons with chronic and disabling conditions: methods, strategies, and issues

Measuring the physical activity of persons with chronic and disabling conditions presents complexities related to measuring instruments, the intensity of the activity being measured, the population being measured, and individual behavior and health status. They often have limitations in mobility that do not preclude physical activity but contribute to the complexity of measuring it, such as slow or altered gait, inability to walk, and the need for assistive devices. This article reviews currently available ways to measure physical activity, describes strengths and weaknesses of various measures, and provides examples of complexities in measuring physical activity in people who move differently.

Effect of combined movement and heart rate monitor placement on physical activity estimates during treadmill locomotion and free-living

A placement effect on activity measures from movement sensors has been reported during treadmill and free-living activity. Positioning of electrodes may impact on movement artifact susceptibility as well as surface ECG waveform amplitudes and thus potentially on the precision by which heart rate (HR) is ascertained from such ECG traces. The purpose of this study was to examine the extent to which placement of the combined HR and movement sensor, Actiheart, influences measurement of HR and movement, and estimates of energy expenditure. A total of 24 participants (20-39 years, 45-109 kg, 1.54-2.05 m, 19-29 kg m(-2)) were recruited. Whilst wearing two monitors, one placed at the level of the third intercostal space (upper position) and one just below the apex of the sternum (lower position), study participants performed level walking, incline walking, and level running on treadmill, and completed at least one day of free-living monitoring. Placement differences in HR data quality, movement counts, and energy expenditure (estimated from combined HR and movement) were analyzed with regression techniques. Quality of HR data was generally higher when monitors were placed in the lower position. This effect was more pronounced in men during both treadmill activity (relative risk, RR [95% CI] of noisy HR data in upper vs. lower position, RR=1.3[0.3; 5.6] in women, RR=174[14; 2,156] in men) and during free-living (RR=1.2[0.4; 3.3] in women, RR=25[9.6; 67] in men). There were minor placement differences (< or =8%) in movement counts only in women during incline walking and running. During free-living, no placement effect on counts was observed. In all test scenarios, estimates of energy expenditure from the two positions were not significantly different. Positioning the Actiheart at the level below the sternum may yield cleaner HR data. Regardless of which position is used, this has little or no effect on movement counts and energy expenditure estimates, which is encouraging for studies where research participants may have to position the monitors themselves.

Reliability and validity of the combined heart rate and movement sensor Actiheart

Accurate quantification of physical activity energy expenditure is a key part of the effort to understand disorders of energy metabolism. The Actiheart, a combined heart rate (HR) and movement sensor, is designed to assess physical activity in populations.

OBJECTIVE

To examine aspects of Actiheart reliability and validity in mechanical settings and during walking and running.

METHODS

In eight Actiheart units, technical reliability (coefficients of variation, CV) and validity for movement were assessed with sinusoid accelerations (0.1-20 m/s(2)) and for HR by simulated R-wave impulses (25-250 bpm). Agreement between Actiheart and ECG was determined during rest and treadmill locomotion (3.2-12.1 km/h). Walking and running intensity (in J/min/kg) was assessed with indirect calorimetry in 11 men and nine women (26-50 y, 20-29 kg/m(2)) and modelled from movement, HR, and movement + HR by multiple linear regression, adjusting for sex.

RESULTS

Median intrainstrument CV was 0.5 and 0.03% for movement and HR, respectively. Corresponding interinstrument CV values were 5.7 and 0.03% with some evidence of heteroscedasticity for movement. The linear relationship between movement and acceleration was strong (R(2) = 0.99, P < 0.001). Simulated R-waves were detected within 1 bpm from 30 to 250 bpm. The 95% limits of agreement between Actiheart and ECG were -4.2 to 4.3 bpm. Correlations with intensity were generally high (R(2) > 0.84, P < 0.001) but significantly highest when combining HR and movement (SEE < 1 MET).

CONCLUSIONS

The Actiheart is technically reliable and valid. Walking and running intensity may be estimated accurately but further studies are needed to assess validity in other activities and during free-living.

SPONSORSHIP

The study received financial support from the Wellcome Trust and SB was supported by a scholarship from Unilever, UK.

Comparison of accuracy and precision of heart rate calibration methods to estimate total carbon dioxide production during 13C-breath tests

BACKGROUND

13C-breath tests are noninvasive tools to measure gastrointestinal function and nutritional interventions. Calculation of percentage dose recovered of 13C in exhaled breath requires knowledge of CO2 production rate (VC02). A resting value is usually assumed, but this can underestimate VC02 because subjects are unlikely to remain at rest during tests that last for many hours. There is a need for a method to estimate nonresting VC02 during 13C-breath tests.

OBJECTIVE

To calibrate a heart rate monitor to continually estimate VC02 during 13C-breath tests.

DESIGN

Proof of concept study.

SUBJECTS

Eight healthy adults, 10 healthy children and six children with cystic fibrosis.

METHODS

Heart rate and VC02 were measured simultaneously at resting and nonresting levels. A new calibration method (smoothing heart rate and fitting a sigmoid function) was compared with published methods. A [ 3C]acetate breath test was used to demonstrate the range of physical activity during breath tests.

RESULTS

The new calibration method was more accurate than existing methods (mean bias -0.0002%, 95% confidence interval (CI) -0.0007, 0.0003% of the mean measured VC02). Smoothing heart rate gave a more precise estimate of VC02 and a more accurate estimate of resting energy expenditure (mean bias -0.09, 95% Cl -0.22, 0.05 mmol CO2 min-' m-2 body surface area) than using raw data (mean bias -0.21, 95% Cl -0.38, -0.04 mmol CO2 min' m-2 body surface area). Physical activity level ranged from 1.0 to 2.5 in children, and 1.0 to 1.5 in adults.

CONCLUSION

Use of smoothed HR with a sigmoid function provides an accurate method of estimating nonresting VC02 during 13C-breath tests.

The effect of social desirability and social approval on self-reports of physical activity

The purpose of this investigation was to examine social desirability and social approval as sources of error in three self-reported physical activity assessments using objective measures of physical activity as reference measures. In 1997, women (n = 81) living in Worcester, Massachusetts, completed doubly labeled water measurements and wore an activity monitor for 14 days. They also completed seven interviewer-administered 24-hour physical activity recalls (PARs) and two different self-administered 7-day PARs. Measures of the personality traits "social desirability" and "social approval" were regressed on 1) the difference between physical activity energy expenditure estimated from doubly labeled water and each physical activity assessment instrument and 2) the difference between monitor-derived physical activity duration and each instrument. Social desirability was associated with overreporting of activity, resulting in overestimation of physical activity energy expenditure by 0.65 kcal/kg/day on the second 7-day PAR (95% confidence interval: 0.06, 1.25) and overestimation of activity durations by 4.15-11.30 minutes/day (both 7-day PARs). Social approval was weakly associated with underestimation of physical activity on the 24-hour PAR (-0.15 kcal/kg/day, 95% confidence interval: -0.30, 0.005). Body size was not associated with reporting bias in this study. The authors conclude that social desirability and social approval may influence self-reported physical activity on some survey instruments.

Relativistic parameters of senescence

The laws of biochemistry and biology are governed by parameters whose description in mathematical formulas is based on the three-dimensional space. It is a fact, however, that the life span of a cell and its specific functions, though limited, can be extended or diminished depending on the genetic code but also, on the natural pressure of the environment. The plasticity exhibited by a cellular system has been attributed to the change of the three-dimensional structure of the cell, with time being a simple measure of this change. The model of biological relativity proposed here, considers time as a flexible fourth dimension that corresponds directly to the inertial status of the cells. Two types of clocks are defined: the relativistic biological clock (RBC) and the mechanical clock (MC). In contrast to the MCs that show the astrological reference time, the time shown by the RBCs delay because it depends on cellular activity. The maximum and the expected life span of the cells and/or the organisms can be therefore relied on time transformation. One of the most important factors that can affect time flow is the energy that is produced during metabolic work. Based on this observation, RBCs can be constructed following series of theoretical experiments in order to assess biological time and life span changes.

Accuracy of polar S410 heart rate monitor to estimate energy cost of exercise

PURPOSE

The purpose of this study was to examine the accuracy of the Polar S410 for estimating gross energy expenditure (EE) during exercise when using both predicted and measured VO2max and HRmax versus indirect calorimetry (IC).

METHODS

Ten males and 10 females initially had their VO2max and HRmax predicted by the S410, and then performed a maximal treadmill test to determine their actual values. The participants then performed three submaximal exercise tests at RPE of 3, 5, and 7 on a treadmill, cycle, and rowing ergometer for a total of nine submaximal bouts. For all submaximal testing, the participant had two S410 heart rate monitors simultaneously collecting data: one heart rate monitor (PHRM) utilized their predicted VO2max and HRmax, and one heart rate monitor (AHRM) used their actual values. Simultaneously, EE was measured by IC.

RESULTS

In males, there were no differences in EE among the mean values for the AHRM, PHRM, and IC for any exercise mode (P > 0.05). In females, the PHRM significantly overestimated mean EE on the treadmill (by 2.4 kcal x min(-1)), cycle (by 2.9 kcal x min(-1)), and rower (by 1.9 kcal x min(-1)) (all P < 0.05). The AHRM for females significantly improved the estimation of mean EE for all exercise modes, but it still overestimated mean EE on the treadmill (by 0.6 kcal x min(-1)) and cycle (by 1.2 kcal x min(-1)) (P < 0.05).

CONCLUSION

When the predicted values of VO2max and HRmax are used, the Polar S410 HRM provides a rough estimate of EE during running, rowing, and cycling. Using the actual values for VO2max and HRmax reduced the individual error scores for both genders, but in females the mean EE was still overestimated by 12%.

Comparison of the college alumnus questionnaire physical activity index with objective monitoring

PURPOSE

Two methods of measuring physical activity (PA) were compared over a consecutive 7-day period among 25 adults (12 men and 13 women).

METHODS

Each day estimates of energy expended in light, moderate, vigorous, and total PA were derived from the simultaneous heart-rate motion sensor (HR+M) technique. At the end of the 7-day period participants completed the College Alumnus Questionnaire Physical Activity Index (CAQ-PAI) and results were compared with HR+M technique estimates.

RESULTS

Correlations between the two methods in the four activity categories ranged from r=0.20 to r=0.47, with vigorous and total PA showing higher associations than light and moderate PA. Mean levels of PA (MET-minxwk(-1)) obtained using the two methods were similar in the moderate and vigorous categories, but individual differences were large. Energy expended in light PA was significantly underestimated on the CAQ-PAI, resulting in lower total activity scores on this questionnaire as compared with the HR+M.

CONCLUSIONS

The CAQ-PAI accurately reflected mean moderate and vigorous activity in comparison with the HR+M technique. The results are consistent with other studies which have shown that physical activity questionnaires are better at assessing vigorous PA than ubiquitous light-moderate activities.

Assessment of physical activity by telephone interview versus objective monitoring

PURPOSE

To compare different methods of quantifying time in physical activity (PA).

METHODS

Twenty-five participants (12 male, 13 female) volunteered to be monitored for seven consecutive days, during which different PA patterns were measured by the simultaneous heart-rate motion sensor technique (HR+M). At the end of the 7th day, participants completed questions taken from the 2001 Behavioral Risk Factor Surveillance System (BRFSS) PA module telephone survey, in which they recalled the amount of time spent walking, and in moderate and vigorous activities. The results of the BRFSS PA module were then compared with those of the HR+M.

RESULTS

No significant group differences were found in the amount of time spent in moderate and vigorous activities between methods. However, individual differences were greater for time spent in moderate activities (SE +/- 7.36 min x d(-1); range -70 to 77 min x d(-1)) than for time spent in vigorous activities (SE +/- 3.57 min x d(-1); range -39 to 33 min x d(-1). Spearman correlation coefficients between the HR+M and the BRFSS were significant for vigorous activities (r = 0.54, P < 0.01). There was 80% agreement between the two methods of classifying individuals who either: (a) met the recommendations (through moderate and/or vigorous PA) or (b) did not meet the recommendations.

CONCLUSION

The BRFSS and HR+M methods yielded similar group estimates of PA, but individual assessments of moderate activity differed more than those of vigorous activity. BRFSS estimations of group compliance with national PA recommendations were similar to those of the HR+M.

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.

Estimating human energy expenditure: a review of techniques with particular reference to doubly labelled water

This review includes an historical overview of the techniques for measuring energy expenditure (EE). Following this overview, the "gold standard" method of measuring EE, the doubly labelled water (DLW) method, is emphasised. Other methods, such as direct calorimetry, indirect calorimetry systems, heart rate and EE relationships, questionnaires and activity recall, motion sensors, combined heart rate and motion sensors for the estimation of EE are then highlighted in relation to their validation against the DLW method. The major advantages and disadvantages for each method are then considered. The preferred method to determine EE is likely to depend principally on factors such as the number of study participants to be monitored, the time period of measurements and the finances available. Small study participant numbers over a short period may be measured accurately by means of indirect calorimetric methods (stationary and portable systems). For periods over 3-4 days, EE should ideally be measured using the DLW method. However, the use of motion sensors is very promising in the measurement of EE, and has a number of advantages over the DLW method. Furthermore, if used correctly, both heart rate and questionnaire methods may provide valuable estimates of EE. Additional studies are needed to examine the possibility of improving the accuracy of measurement by combining two or more techniques. Such information, if confirmed by scientific rigour, may lead to an improvement in the estimation of EE and population-based physical activity levels. The accurate measurement of physical activity and EE is critical from both a research and health prospective. A consideration of the relevant techniques used for the estimation of EE may also help improve the quality of these frequently reported measurements.