Simultaneous monitoring of heart rate and motion to assess energy expenditure

Energy Requirements and Nutritional Strategies for Male Soccer Players: A Review and Suggestions for Practice

Soccer is a high intensity intermittent sport, featuring critical events completed at high/maximal intensity which is superimposed onto an aerobic base of lower intensity activities and rest. Due to these varying energic demands and the duration of competition the need for optimal nutritional strategies to offset and delay fatigue are paramount. Over the last 50 years, several investigations have been reported on aspects of soccer be they nutrition-focused or those concerning the demands of the sport. Emanating from these scientific papers, observations have been made on the likely factors which result in the fatigue during match-play. Factors such as muscle glycogen depletion and hypoglycaemia are discussed. Studies on the energy demands of soccer have employed a variety of methodologies which are briefly reviewed and vary between the use of heart rate telemetry to the use of global positioning systems (GPS). Moving on from observations of the energy demands of the sport leads to the major focus of this review which highlights key nutritional strategies to support the preparation and recovery of male soccer players to enhance performance, or at least to enable players to perform adequately. This review examines relevant methodologies in assessing training and competitive energy costs as well as the concomitant energy intakes demanded for successful performance outcomes. In order to bring an applied aspect to the overall findings from areas discussed, some practical ideas of feeding strategies are presented.

Heart Rate Methods Can Be Valid for Estimating Intensity Spectrums of Oxygen Uptake in Field Exercise

Purpose

Quantifying intensities of physical activities through measuring oxygen uptake (V̇O₂) is of importance for understanding the relation between human movement, health and performance. This can in principle be estimated by the heart rate (HR) method, based on the linear relationship between HR and V̇O₂ established in the laboratory. It needs, however, to be explored whether HR methods, based on HR-V̇O₂ relationships determined in the laboratory, are valid for estimating spectrums of V̇O₂ in field exercise. We hereby initiate such studies, and use cycle commuting as the form of exercise.

Methods

Ten male and ten female commuter cyclists underwent measurements of HR and V̇O₂ while performing ergometer cycling in a laboratory and a normal cycle commute in the metropolitan area of Stockholm County, Sweden. Two models of individual HR-V̇O₂ relationships were established in the laboratory through linear regression equations. Model 1 included three submaximal work rates, whereas model 2 also involved a maximal work rate. The HR-V̇O₂ regression equations of the two models were then used to estimate V̇O₂ at six positions of field HR: five means of quintiles and the mean of the whole commute. The estimations obtained were for both models compared with the measured V̇O₂.

Results

The measured quintile range during commuting cycling was about 45–80% of V̇O₂max. Overall, there was a high resemblance between the estimated and measured V̇O₂, without any significant absolute differences in either males or females (range of all differences: −0.03–0.20 L⋅min^–1). Simultaneously, rather large individual differences were noted.

Conclusion

The present HR methods are valid at group level for estimating V̇O₂ of cycle commuting characterized by relatively wide spectrums of exercise intensities. To further the understanding of the external validity of the HR method, there is a need for studying other forms of field exercises.

Exercise load and physical activity intensity in relation to dystonia and choreoathetosis during powered wheelchair mobility in children and youth with dyskinetic cerebral palsy

PURPOSE

To explore the relation between exercise load, physical activity intensity, and movement disorders during powered wheelchair (PW) mobility in people with severe dyskinetic cerebral palsy (DCP).

METHODS

Ten participants with DCP, 6-21 years old, users of a head/foot steering system were included. Dystonia and choreoathetosis were assessed using the Dyskinesia Impairment Mobility Scale (DIMS), heart rate (HR) was used to assess the exercise load of the tasks on the participants, and the accelerometry-based activity index (AI) to measure the physical activity intensity and energy expenditure during mobility task performance.

RESULTS

Neck- and distal arm dystonia showed significant correlations with HR (0.64 < r_s < 0.77; 0.009 < p < 0.048), whereas neck- and proximal arm choreoathetosis with AI (0.64 < r_s < 0.76, 0.011 < p < 0.044). Total-body AI was strongly correlated to the AI of the arms (0.66 < r_s < 0.90,  < 0.001 < p < 0.038), but not to the AI of the head.

CONCLUSIONS

During PW mobility tasks, dystonia is associated to exercise load and choreoathetosis to physical activity intensity and energy expenditure. Findings highlight the difficulties in measuring exercise load and activity intensity in PW users with DCP due to the involuntary hypertonic and/or hyperkinetic hallmark of the movement disorders. Nevertheless, a relaxed surrounding with minimal distractions during PW training may increase learning efficiency. Future studies with a bigger sample size are highly recommended to fully establish the relationship between the variables and to allow generalizability of results.Implications for rehabilitationDystonia is positively related to heart rate during powered mobility, which may be explained by the hypertonic hallmark of dystonia causing an increase in exercise load.Choreoathetosis is positively related to the physical activity index during powered mobility where the hyperkinetic hallmark of choreoathetosis may lead to an increase in physical activity intensity and energy expenditure.Arm overflow movements are the component which contribute the most to total-body activity index, thus, minimizing these movements may lower the overall energy expenditure during powered mobility.Mobility training in a relaxed surrounding with minimal distractions and minimized arm overflow movements may lead to a less-demanding powered wheelchair mobility experience and increased learning efficiency.

A nonlinear mixed model approach to predict energy expenditure from heart rate

Objective.Heart rate (HR) monitoring provides a convenient and inexpensive way to predict energy expenditure (EE) during physical activity. However, there is a lot of variation among individuals in the EE-HR relationship, which should be taken into account in predictions. The objective is to develop a model that allows the prediction of EE based on HR as accurately as possible and allows an improvement of the prediction using calibration measurements from the target individual.Approach.We propose a nonlinear (logistic) mixed model for EE and HR measurements and an approach to calibrate the model for a new person who does not belong to the dataset used to estimate the model. The calibration utilizes the estimated model parameters and calibration measurements of HR and EE from the person in question. We compare the results of the logistic mixed model with a simpler linear mixed model for which the calibration is easier to perform.Main results.We show that the calibration is beneficial already with only one pair of measurements on HR and EE. This is an important benefit over an individual-level model fitting, which requires a larger number of measurements. Moreover, we present an algorithm for calculating the confidence and prediction intervals of the calibrated predictions. The analysis was based on up to 11 pairs of EE and HR measurements from each of 54 individuals of a heterogeneous group of people, who performed a maximal treadmill test.Significance.The proposed method allows accurate energy expenditure predictions based on only a few calibration measurements from a new individual without access to the original dataset, thus making the approach viable for example on wearable computers.

Assessment of energy expenditure during high intensity cycling and running using a heart rate and activity monitor in young active adults

Objective

Although high intensity physical activities may represent a great proportion of the total energy expenditure in active people, only sparse studies have investigated the accuracy of wearable monitors to assess activity related energy expenditure (AEE) during high intensity exercises. Therefore, the purpose of the present study was to investigate the accuracy of the Actiheart, a light portable monitor estimating AEE based on heart rate (HR) and activity counts (ACT), during two popular activities (running and cycling) performed at high intensities. The benefit of an individual calibration of the HR-AEE relationship established during a preliminary maximal test was also evaluated.

Methods

AEE was estimated in eighteen active adults (4 women and 14 men; 25 ± 4 yr) with indirect calorimetry using a respiratory gas analysis system (reference method) and the Actiheart during 5-min running and cycling at 60, 75 and 85% of maximal oxygen uptake (VO₂max) previously determined during a maximal test performed on a treadmill or cycle ergometer. For the Actiheart, AEE was estimated either using the group or individual calibrated equations available in the dedicated software, and their respective HR, ACT or combined HR/ACT algorithms.

Results

When the HR algorithm was used for cycling and the HR or HR/ACT algorithms for running, AEE measured by the Actiheart increased proportionally to exercise intensity from 60 to 85% VO₂max (P<0.001). Compared to indirect calorimetry, the Actiheart group calibrated equations slightly to moderately underestimated (3 to 20%) AEE for the three exercise intensities (P<0.001). Accuracy of AEE estimation was greatly improved by individual calibration of the HR-AEE relationship (underestimation below 5% and intraclass correlation coefficient [ICC]: 0.79–0.93) compared to group calibration (ICC: 0.64–0.79).

Conclusion

The Actiheart enables to assess AEE during high intensity running and cycling when the appropriate algorithm is applied. Since an underestimation was present for group calibration, an individual and sport-specific calibration should be performed when a high accuracy is required.

The heart rate method for estimating oxygen uptake: analyses of reproducibility using a range of heart rates from commuter walking

Background

The heart rate method, based on the linear relation between heart rate and oxygen uptake, is potentially valuable to monitor intensity levels of physical activities. However, this depends not least on its reproducibility under standard conditions. This study aims, therefore, to evaluate the reproducibility of the heart rate method in the laboratory using a range of heart rates associated with walking commuting.

Methods

On two different days, heart rate and oxygen uptake measurements were made during three submaximal (model 1) and a maximal exercise intensity (model 2) on a cycle ergometer in the laboratory. 14 habitual walking commuters participated. The reproducibility, based on the regression equations from test and retest and using three levels of heart rate from the walking commuting, was analyzed. Differences between the two models were also analyzed.

Results

For both models, there were no significant differences between test and retest in the constituents of the regression equations (y intercept, slope and r value). Neither were there any systematic differences in estimated absolute levels of VO₂ between test and retest for either model. However, some rather large individual differences were seen in both models. Furthermore, no significant differences were seen between the two models in slopes, intercepts and r values of the regression equations or in the estimated VO₂.

Conclusion

The heart rate method shows good reproducibility on the group level in estimating oxygen consumption from heart rate–oxygen uptake relations in the laboratory, and based on three levels of heart rate which are representative for walking commuting.

Accuracy of the Multisensory Wristwatch Polar Vantage's Estimation of Energy Expenditure in Various Activities: Instrument Validation Study

Background

Sport watches and fitness trackers provide a feasible way of obtaining energy expenditure (EE) estimations in daily life as well as during exercise. However, today’s popular wrist-worn technologies show only poor-to-moderate EE accuracy. Recently, the invention of optical heart rate measurement and the further development of accelerometers in wrist units have opened up the possibility of measuring EE.

Objective

This study aimed to validate the new multisensory wristwatch Polar Vantage and its EE estimation in healthy individuals during low-to-high-intensity activities against indirect calorimetry.

Methods

Overall, 30 volunteers (15 females; mean age 29.5 [SD 5.1] years; mean height 1.7 [SD 0.8] m; mean weight 67.5 [SD 8.7] kg; mean maximal oxygen uptake 53.4 [SD 6.8] mL/min·kg) performed 7 activities—ranging in intensity from sitting to playing floorball—in a semistructured indoor environment for 10 min each, with 2-min breaks in between. These activities were performed while wearing the Polar Vantage M wristwatch and the MetaMax 3B spirometer.

Results

After EE estimation, a mean (SD) of 69.1 (42.7) kcal and 71.4 (37.8) kcal per 10-min activity were reported for the MetaMax 3B and the Polar Vantage, respectively, with a strong correlation of r=0.892 (P<.001). The systematic bias was 2.3 kcal (3.3%), with 37.8 kcal limits of agreement. The lowest mean absolute percentage errors were reported during the sitting and reading activities (9.1%), and the highest error rates during household chores (31.4%). On average, 59.5% of the mean EE values obtained by the Polar Vantage were within ±20% of accuracy when compared with the MetaMax 3B. The activity intensity quantified by perceived exertion (odds ratio [OR] 2.028; P<.001) and wrist circumference (OR −1.533; P=.03) predicted 29% of the error rates within the Polar Vantage.

Conclusions

The Polar Vantage has a statistically moderate-to-good accuracy in EE estimation that is activity dependent. During sitting and reading activities, the EE estimation is very good, whereas during nonsteady activities that require wrist and arm movement, the EE accuracy is only moderate. However, compared with other available wrist-worn EE monitors, the Polar Vantage can be recommended, as it performs among the best.

Reliability of Zephyr Bioharness and Fitbit Charge Measures of Heart Rate and Activity at Rest, During the Modified Canadian Aerobic Fitness Test, and Recovery

Nazari, G, MacDermid, JC, Sinden, KE, Richardson, J, and Tang, A. Reliability of Zephyr Bioharness and Fitbit Charge measures of heart rate and activity at rest, during the modified Canadian Aerobic Fitness Test, and recovery. J Strength Cond Res 33(2): 559-571, 2019-The purpose of this study was to determine the intrasession and intersession reliability of Zephyr Bioharness (ZB) and Fitbit Charge variables in both healthy men and women at rest, during the Modified Canadian Aerobic Fitness Test (mCAFT), and throughout recovery. Stratified convenience and snowball sampling were used to recruit 60 participants (30 women, 48 ± 15 years) and (30 men, 48 ± 15 years) from McMaster University student, staff, and faculty population. At rest, intrasession average heart rate (b·min). Intraclass correlation coefficients (ICCs) and Standard Error of Measurement [SEM] for Zephyr ranged from (0.94-0.97) [1.17-1.70] to (0.92-0.97) [1.45-2.10] for Fitbit Charge. During the mCAFT, the Zephyr ICCs and (SEM) ranged from 0.31-0.99 (1.28-8.10) to 0.45-0.99 (1.45-8.71) for the Fitbit Charge. Throughout the recovery, the ICCs and (SEM) ranged from 0.44-0.98 (1.26-10.47) to 0.45-0.98 (1.15-11.90) for Zephyr and Fitbit devices, respectively. At rest, intersession ICCs (SEM) for Zephyr and Fitbit ranged from 0.90-0.94 (1.73-2.37) to 0.88-0.94 (1.83-2.67), respectively. At mCAFT, the Zephyr ICCs (SEM) ranged from 0.91-0.97 (3.12-4.64) to 0.85-0.98 (3.28-4.88) for the Fitbit. Throughout the recovery, the ICCs (SEM) ranged from 0.93-0.97 (2.65-4.66) to 0.76-0.91 (3.17-4.67) for Zephyr and Fitbit devices, respectively. To conclude, both the ZB and Fitbit Charge devices demonstrated excellent reliability measures throughout the 3 phases. The findings from our study add to the existing pool of literature regarding the reliability parameters of wearable devices and suggest that stable and consistent measures of heart rate and physical activity can be obtained using ZB and Fitbit Charge devices among healthy male and female participants at rest, during a standardized submaximal fitness test (mCAFT), and throughout recovery.

The heart rate method for estimating oxygen uptake: Analyses of reproducibility using a range of heart rates from cycle commuting

Monitoring aerobic exercise intensities of free-living physical activities is valuable for purposes such as education and research. The heart rate (HR) method, based on the linear relation between HR and oxygen uptake (VO₂), is potentially valuable for this purpose. Three prerequisites are that the method is reproducible, and valid for the specific form of physical activity executed as well as under field conditions. The aim of this study is to evaluate reproducibility of the heart rate method in the laboratory. VO₂ and HR measurements were made on two different occasions during three submaximal (model 1) plus a maximal exercise intensity (model 2) on a cycle ergometer in the laboratory. 19 habitual commuter cyclists (9 males and 10 females), aged 44 ± 3 years, were measured. The reproducibility of the estimated VO₂, based on three levels of HR from commuting cycling and the regression equations from test and retest were analyzed. Differences between the two models were also studied. For both models, there were no significant differences between test and retest in the constituents of the regression equations (y-intercept, slope and r-value). Neither were there any systematic differences in estimated absolute levels of VO₂ between test and retest. The relative differences between test and retest, based on estimations from three different levels of HR, were 0.99 ± 11.0 (n.s.), 2.67 ± 6.48 (n.s.) and 3.57 ± 6.24% (p<0.05) for model 1, and 1.09 ± 10.6, 1.75 ± 6.43 and 2.12 ± 5.92% (all n.s.) for model 2. However, some large individual differences were seen in both models. There were no significant differences between the two models in the slopes, intercepts or r-values of the regression equations or in the estimated levels of VO₂. The heart rate method shows good reproducibility on the group level in estimating oxygen consumption from HR-VO₂ relations in the laboratory, and based on three levels of HR which are representative for cycle commuting. However, on the individual level, some large variations were seen.

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.

Occupational physical activity in brewery and office workers

Active lifestyles are beneficial to health and well-being but our workplaces may not be inherently supportive of physical activity at work. With the increasing use of technology in the workplace, many jobs are becoming more sedentary. The purpose of this study was to characterize levels of occupational physical activity (OPA) among active and sedentary workers. Two types of activity trackers (Fitbit Charge HR and Hexoskin) were used to assess activity measures (steps, heart rate, and energy expenditure) among workers during one full work shift. The first objective of the study was to assess the agreement between two types of accelerometer-based activity trackers as measures of occupational physical activity. The second objective of this study was to assess differences in measures of OPA among workers in generally physically active (brewery) and sedentary (office) work environments. Occupational physical activity data were collected from 50 workers in beer-brewing tasks and 51 workers in office work tasks. The 101 subjects were from the brewing service sector, a call center, and an engineering office within a manufacturing facility. A two-factor repeated measures analysis of variance (ANOVA) was used to assess the two activity tracking devices while two-sample t-tests were used to compare the two worker groups. There were statistically significant differences in total steps and mean heart rate between the two devices. When comparing the two groups of workers there were statistically significant differences in measures of steps, mean heart rate, and energy expenditure. The results of the present study provide quantitative evidence that levels of OPA should be identified for different work groups.

Accuracy of a Wrist-Worn Heart Rate Sensing Device during Elective Pediatric Surgical Procedures

The reliability of wearable photoplethysmography (PPG) sensors to measure heart rate (HR) in hospitalized patients has only been demonstrated in adults. We evaluated the accuracy of HR monitoring with a personal fitness tracker (PFT) in children undergoing surgery. HR monitoring was performed using a wrist-worn PFT (Fitbit Charge HR) in 30 children (8.21 ± 3.09 years) undergoing laparoscopy (n = 8) or open surgery (n = 22). HR values were analyzed preoperatively and during surgery. The accuracy of HR recordings was compared with measurements recorded during continuous electrocardiographic (cECG) monitoring; HRs derived from continuous monitoring with pulse oximetry (SpO2R) were used as a positive control. PFT-derived HR values were in agreement with those recorded during cECG (r = 0.99) and SpO2R (r = 0.99) monitoring. PFT performance remained high in children < 8 years (r = 0.99), with a weight < 30 kg (r = 0.99) and when the HR was < 70 beats per minute (bpm) (r = 0.91) or > 140 bpm (r = 0.99). PFT accuracy was similar during laparoscopy and open surgery, as well as preoperatively and during the intervention (r > 0.9). PFT–derived HR showed excellent accuracy compared with HRs measured by cECG and SpO2R during pediatric surgical procedures. Further clinical evaluation is needed to define whether PFTs can be used in different health care settings.

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 and Energy Expenditure in Wheelchair Users: Methods, Considerations and Future Directions

Accurately measuring physical activity and energy expenditure in persons with chronic physical disabilities who use wheelchairs is a considerable and ongoing challenge. Quantifying various free-living lifestyle behaviours in this group is at present restricted by our understanding of appropriate measurement tools and analytical techniques. This review provides a detailed evaluation of the currently available measurement tools used to predict physical activity and energy expenditure in persons who use wheelchairs. It also outlines numerous considerations specific to this population and suggests suitable future directions for the field. Of the existing three self-report methods utilised in this population, the 3-day Physical Activity Recall Assessment for People with Spinal Cord Injury (PARA-SCI) telephone interview demonstrates the best reliability and validity. However, the complexity of interview administration and potential for recall bias are notable limitations. Objective measurement tools, which overcome such considerations, have been validated using controlled laboratory protocols. These have consistently demonstrated the arm or wrist as the most suitable anatomical location to wear accelerometers. Yet, more complex data analysis methodologies may be necessary to further improve energy expenditure prediction for more intricate movements or behaviours. Multi-sensor devices that incorporate physiological signals and acceleration have recently been adapted for persons who use wheelchairs. Population specific algorithms offer considerable improvements in energy expenditure prediction accuracy. This review highlights the progress in the field and aims to encourage the wider scientific community to develop innovative solutions to accurately quantify physical activity in this population.

Measurement Methods for Physical Activity and Energy Expenditure: a Review

Physical activity is defined as any bodily movement produced by skeletal muscles that results in energy expenditure. The benefits of physical activity for health maintenance have been well documented, especially in the prevention and management of chronic diseases. Therefore, accurate measurement of physical activity and energy expenditure is essential both for epidemiological studies and in the clinical context. Given the large number of available methods, it is important to have an understanding of each, especially when one needs to choose a technique to use. The purpose of this review was to discuss the components of total energy expenditure and present advantage and limitations of different methods of physical activity and energy expenditure assessment.

Measuring energy expenditure using heart rate to assess the effects of wheelchair tyre pressure

Objective: To investigate the effects of wheelchair tyre pressure on mechanics and energy and explore the use of heart rate as a measurement of energy expenditure.

Design: A single factor repeated measures design was used. Four tyre pressures (100, 75, 50, 25 psi) represented a change of workload. Each subject wheeled at a constant self-selected wheeling velocity for 8 min. A total of four trials were completed with a 10-min rest between trials. Oxygen consumption, heart rate and distance travelled were collected during each trial.

Subjects: Three women and 11 men with spinal cord injury. The mean age for the whole group was 34.5 years. The range of lesion level was T4-L1.

Results: There was a significant increase is energy expenditure when tyres were deflated to 50 psi from 100 psi. The mean correlation between heart rate and oxygen consumption was 0.74 for all subjects. For the subjects with lesions above T6 and T6 and below the correlations were 0.55 and 0.82, respectively.

Conclusions: Tyre pressures below 50% inflation add an additional 25% increase in energy expenditure during wheeling. This could be detected using oxygen consumption or heart rate, as heart rate was shown to have a good correlation with oxygen consumption in the spinal cord injured with lesions below T5. Heart rate does have its limitations and it should only be used to measure within-subject differences.

Accuracy of Heart Rate Watches: Implications for Weight Management

Background

Wrist-worn monitors claim to provide accurate measures of heart rate and energy expenditure. People wishing to lose weight use these devices to monitor energy balance, however the accuracy of these devices to measure such parameters has not been established.

Aim

To determine the accuracy of four wrist-worn devices (Apple Watch, Fitbit Charge HR, Samsung Gear S and Mio Alpha) to measure heart rate and energy expenditure at rest and during exercise.

Methods

Twenty-two healthy volunteers (50% female; aged 24 ± 5.6 years) completed ~1-hr protocols involving supine and seated rest, walking and running on a treadmill and cycling on an ergometer. Data from the devices collected during the protocol were compared with reference methods: electrocardiography (heart rate) and indirect calorimetry (energy expenditure).

Results

None of the devices performed significantly better overall, however heart rate was consistently more accurate than energy expenditure across all four devices. Correlations between the devices and reference methods were moderate to strong for heart rate (0.67–0.95 [0.35 to 0.98]) and weak to strong for energy expenditure (0.16–0.86 [-0.25 to 0.95]). All devices underestimated both outcomes compared to reference methods. The percentage error for heart rate was small across the devices (range: 1–9%) but greater for energy expenditure (9–43%). Similarly, limits of agreement were considerably narrower for heart rate (ranging from -27.3 to 13.1 bpm) than energy expenditure (ranging from -266.7 to 65.7 kcals) across devices.

Conclusion

These devices accurately measure heart rate. However, estimates of energy expenditure are poor and would have implications for people using these devices for weight loss.

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.

Adaptive neuro-fuzzy inference systems with k-fold cross-validation for energy expenditure predictions based on heart rate

This paper presents a new model based on adaptive neuro-fuzzy inference systems (ANFIS) to predict oxygen consumption (V˙O2) from easily measured variables. The ANFIS prediction model consists of three ANFIS modules for estimating the Flex-HR parameters. Each module was developed based on clustering a training set of data samples relevant to that module and then the ANFIS prediction model was tested against a validation data set. Fifty-eight participants performed the Meyer and Flenghi step-test, during which heart rate (HR) and V˙O2 were measured. Results indicated no significant difference between observed and estimated Flex-HR parameters and between measured and estimated V˙O2 in the overall HR range, and separately in different HR ranges. The ANFIS prediction model (MAE = 3 ml kg(-1) min(-1)) demonstrated better performance than Rennie et al.'s (MAE = 7 ml kg(-1) min(-1)) and Keytel et al.'s (MAE = 6 ml kg(-1) min(-1)) models, and comparable performance with the standard Flex-HR method (MAE = 2.3 ml kg(-1) min(-1)) throughout the HR range. The ANFIS model thus provides practitioners with a practical, cost- and time-efficient method for V˙O2 estimation without the need for individual calibration.

Assessment of physical activity and energy expenditure: an overview of objective measures

The ability to assess energy expenditure (EE) and estimate physical activity (PA) in free-living individuals is extremely important in the global context of non-communicable diseases including malnutrition, overnutrition (obesity), and diabetes. It is also important to appreciate that PA and EE are different constructs with PA defined as any bodily movement that results in EE and accordingly, energy is expended as a result of PA. However, total energy expenditure, best assessed using the criterion doubly labeled water (DLW) technique, includes components in addition to physical activity energy expenditure, namely resting energy expenditure and the thermic effect of food. Given the large number of assessment techniques currently used to estimate PA in humans, it is imperative to understand the relative merits of each. The goal of this review is to provide information on the utility and limitations of a range of objective measures of PA and their relationship with EE. The measures discussed include those based on EE or oxygen uptake including DLW, activity energy expenditure, physical activity level, and metabolic equivalent; those based on heart rate monitoring and motion sensors; and because of their widespread use, selected subjective measures.

The Impact of Accelerometer and Heart Rate Data on Hypoglycemia Mitigation in Type 1 Diabetes

Aerobic exercise can lower blood glucose levels and alter insulin sensitivity both during and several hours after exercise, creating challenges for a closed-loop artificial pancreas. Predictive low glucose suspend (PLGS) algorithms are a first step toward an artificial pancreas, but few of these have been successfully applied to exercise. This study incorporates physical activity measurements from a combined accelerometer/heart rate monitor (HRM) to improve the performance of an existing PLGS algorithm at mitigating exercise-associated hypoglycemia in participants with type 1 diabetes. In all, 22 subjects with type 1 diabetes on insulin pump therapy were provided a combined accelerometer/HRM and (if not already using one) a continuous glucose monitor (CGM), then instructed to go about their everyday lives while wearing the devices. After the monitoring period, each subject's insulin pump, CGM, and accelerometer/HRM were downloaded and the data were used to augment an existing PLGS algorithm to incorporate activity. Using a computer simulator, the accelerometer-augmented algorithm was compared to the HRM-augmented algorithm to determine which was most effective at mitigating hypoglycemia. Mean length of monitoring was 4.9 days. Across all subjects, 11 061 CGM readings were recorded during the monitoring period. In the simulator analysis, the PLGS algorithm reduced hypoglycemia by 62%, compared to 71% and 74% reductions for the HRM-augmented and accelerometer-augmented algorithms, respectively; combined accelerometer and HRM augmentation provided a 76% reduction. In a simulated setting, the accelerometer-augmented pump suspension algorithm decreases the incidence of exercise-related hypoglycemia by a meaningful amount compared to the PLGS algorithm alone. Results also failed to justify the additional user burden of a HRM.

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.

Wheeling efficiency: The effects of varying tyre pressure with children and adolescents

PURPOSE

Clinicians often observe child wheelchair users wheeling on tyres that are not inflated to manufacturer's recommendations. The purpose of this study was to investigate changes in energy expenditure that are related to decreased tyre pressure.

METHODS

A within subject repeated measures design was used to assess the energy requirements of wheeling with four randomized tire inflation levels (25, 50, 75 and 100% of recommended tire pressure, 100 psi). All 10 subjects (mean age 14.2 +/- 2.3 years completed four 5-minute trials (one for each tyre pressure), while wheeling at a constant, self-selected velocity. Heart rate and wheeling velocity were measured.

RESULTS

There was no change in wheeling velocity with changes in tyre pressure; however, energy expenditure was found to increase by over 15% with decreasing tyre pressure (p < 0.05).

CONCLUSIONS

In order for children to minimize their energy expenditure and, thus, improve their independence, clinicians and parents must be educated as to the importance of regular wheelchair tyre inflation regimes.

[Evaluation of energy expenditure in children. Physiological and clinical implications and measurement methods]

The present article reviews the importance of the study of energy metabolism and its methods of assessment in children. Classically, energy requirements have been assessed by predictive equations based on anthropometric data. However, there are several physiologic and pathogenic states that may cause discrepancies between estimated and real values and consequently direct measurements of energy expenditure should be used. The gold standard to assess total energy expenditure during prolonged periods is the doubly labeled water method, which is mainly used for research studies. The best approach for resting energy expenditure determination in the clinical setting is indirect calorimetry. However, this method does not provide data on energy consumption under free-living conditions and its use in some critical care patients is restricted by technical limitations. Several other approaches to assess activity have been developed, based on heart rate, body temperature measurements, motion sensors and combined methods.

Distribution and determinants of maximal physical work capacity of Korean male metal workers

The distribution of maximal physical work capacity (MPWC) can be used to establish an upper limit for energy expenditure during work (EEwork). If physically demanding work has wearing effects, there will be a negative relationship between MPWC and workload. This study was conducted to investigate the distribution of MPWC among Korean metal workers and to examine the relationship between workload and MPWC. MPWC was estimated with a bicycle ergometer using a submaximal test. Energy expenditure was estimated by measuring heart rates during work. The study subjects were 507 male employees from several metal industries in Korea. They had a lower absolute VO2max than the Caucasian populations described in previous studies. The older workers had a lower physical capacity and a greater overload at work. A negative relationship was found between MPWC and workload across all age groups. Upper limits for EEwork for all age groups and for older age groups are recommended based on the 5th percentile value of MPWC.

Activity monitoring and energy expenditure in COPD patients: a validation study

There is increasing interest in the objective measurement of physical activity in chronic obstructive pulmonary disease (COPD) patients due to the close relationship between physical activity level, health, disability and mortality. We aimed to (a) determine the validity and reproducibility of an activity monitor that integrates accelerometry with multiple physiologic sensors in the determination of energy expenditure in COPD subjects and (b) to document the independent contribution of the additional physiologic sensors to accelerometry measures in improving true energy expenditure determination. Eight subjects (4 male, FEV(1) 56.4 +/- 14.1%, RV 145.0 +/- 75.7%) performed 2 separate 6-minute walk and 2 incremental shuttle walk exercise tests. Energy expenditure was calculated during each exercise test using the physiologic activity monitor and compared to a validated exhaled breath metabolic system. Test-retest reproducibility of physiologic activity monitor during the walking tests was comparable to an exhaled breath metabolic system. Physiologic sensor data significantly improved the explained variance in energy expenditure determination (r(2)=0.88) compared to accelerometry data alone (r(2)=0.68). This physiologic activity monitor provides a valid and reproducible estimate of energy expenditure during slow to moderate paced walking in a laboratory setting and represents an objective method to assess activity in COPD subjects.

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.

Prediction of energy expenditure from heart rate monitoring during submaximal exercise

The aims of this study were to quantify the effects of factors such as mode of exercise, body composition and training on the relationship between heart rate and physical activity energy expenditure (measured in kJ x min(-1)) and to develop prediction equations for energy expenditure from heart rate. Regularly exercising individuals (n = 115; age 18-45 years, body mass 47-120 kg) underwent a test for maximal oxygen uptake (VO2max test), using incremental protocols on either a cycle ergometer or treadmill; VO2max ranged from 27 to 81 ml x kg(-1) x min(-1). The participants then completed three steady-state exercise stages on either the treadmill (10 min) or the cycle ergometer (15 min) at 35%, 62% and 80% of VO2max, corresponding to 57%, 77% and 90% of maximal heart rate. Heart rate and respiratory exchange ratio data were collected during each stage. A mixed-model analysis identified gender, heart rate, weight, V2max and age as factors that best predicted the relationship between heart rate and energy expenditure. The model (with the highest likelihood ratio) was used to estimate energy expenditure. The correlation coefficient (r) between the measured and estimated energy expenditure was 0.913. The model therefore accounted for 83.3% (R2) of the variance in energy expenditure in this sample. Because a measure of fitness, such as VO2max, is not always available, a model without VO2max included was also fitted. The correlation coefficient between the measured energy expenditure and estimates from the mixed model without VO2max was 0.857. It follows that the model without a fitness measure accounted for 73.4% of the variance in energy expenditure in this sample. Based on these results, we conclude that it is possible to estimate physical activity energy expenditure from heart rate in a group of individuals with a great deal of accuracy, after adjusting for age, gender, body mass and fitness.

Self-selected exercise intensity during household/garden activities and walking in 55 to 65-year-old females

This study determined whether some of the more vigorous household and garden tasks (sweeping, window cleaning, vacuuming and lawn mowing) were performed at a moderate intensity (3-6 METs or metabolic equivalents) by a representative sample of 50, 55 to 65-year-old women (X +/- SD; 59.3 +/- 3.1 years, 161.5 +/- 5.2 cm, 69.4 +/- 12.4 kg, 38.4 +/- 7.3% BF). Data collection was conducted in a standardised laboratory environment and in the subjects' homes. Energy expenditure during self-perceived moderate paced walking around a quadrangle was also used as a marker of exercise intensity. Energy expenditure measured via indirect calorimetry was also predicted from: HR, CSA accelerometer counts, Quetelet's index and the Borg rating of perceived exertion. Ninety-six percent of the subjects walked at an intensity of >or= 3.0 METs. Except for vacuuming in the laboratory (X = 2.9 METs; P = 0.19), the intensity of each of the other activities was significantly (P </or= 0.002) greater than 3.0 METs. Subjects swept (3.7 vs. 3.3 METs) and vacuumed (3.6 vs. 2.9 METs) at greater intensities in the home than in the laboratory, whereas the converse applied to window cleaning (3.3 vs. 3.6 METs) and lawn mowing (4.9 vs. 5.5 METs). Eighty-six percent (172 out of 200) of the VO2 measurements were >or= 3.0 METs when the four household/garden activities were performed in the subjects' homes. These activities therefore have the potential to contribute to the 30 min day(-1) of moderate intensity physical activity required to confer health benefits but there was much inter-individual variability in the intensity at which these tasks were performed. Random intercept regression analyses yielded prediction equations with 95% confidence intervals of +/- 0.80 and +/- 0.84 METs for the laboratory and home based equations, respectively. Considering the means for the five activities ranged from 2.9 to 5.5 METs, these 95% confidence intervals lack predictive precision at the individual level. Nevertheless, the laboratory and home-based equations predicted with correct classification rates of 89 and 90%, respectively, whether energy expenditure was < 3.0 or >or= 3.0 METs.

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.

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.

Assessment of human locomotion by using an insole measurement system and artificial neural networks

A new method for measuring and characterizing free-living human locomotion is presented. A portable device was developed to objectively record and measure foot-ground contact information in every step for up to 24h. An artificial neural network (ANN) was developed to identify the type and intensity of locomotion. Forty subjects participated in the study. The subjects performed level walking, running, ascending and descending stairs at slow, normal and fast speeds determined by each subject, respectively. The device correctly identified walking, running, ascending and descending stairs (accuracy 98.78%, 98.33%, 97.33%, and 97.29% respectively) among different types of activities. It was also able to determine the speed of walking and running. The correlation between actual speed and estimated speed is 0.98, p< 0.0001. The average error of walking and running speed estimation is -0.050+/-0.747 km/h (mean +/- standard deviation). The study has shown the measurement of duration, frequency, type, and intensity of locomotion highly accurate using the new device and an ANN. It provides an alternative tool to the use of a gait lab to quantitatively study locomotion with high accuracy via a small, light and portable device, and to do so under free-living conditions for the clinical applications.

A Novel Energy Expenditure Prediction Equation for Intermittent Physical Activity

PURPOSE

This study was designed to characterize the relative contributions of the current heart rate (HR) and HR in the previous minute to predict energy expenditure (EE) and to model these and other determinants to predict EE during intermittent activity.

METHODS

Regularly exercising subjects (N=65, 36+/-9 yr, body mass index (BMI), 23.9+/- 2.5 kg.m) were tested. Body composition and maximal oxygen uptake (& OV0312;O2) were measured. On a separate day, subjects completed 12 x 4-min workloads, separated by 4 x 1-min rest periods, during which HR and & OV0312;O2 were continuously monitored. Using the intermittent activity calibration data, an EE model was developed. For validation, the final model was used to predict EE in a separate sample (N=17), who completed a gym training exercise session. Using the HR data, EE data were estimated using (a) the new model and were compared with (b) a previous model developed using a continuous incremental calibration test.

RESULTS

The following variables contributed significantly to the estimation of EE during intermittent activity: age, gender, & OV0312;O2 max, current minute's HR, previous minute's HR, and an interaction variable consisting of previous minute's HR and & OV0312;O2 max. The final model yielded an R of 82% for the comparison of predicted and measured EE. When this model was applied to an independent sample for validation (N=17), improvements in EE prediction, when compared to the existing model (b), were most apparent during free-living non-continuous exercise.

CONCLUSION

It is possible to improve the accuracy of predicting EE from HR, by incorporating both & OV0312;O2 max and the previous minute's HR in the prediction model.

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.

The energy cost of household and garden activities in 55- to 65-year-old males

This study measured the energy expenditure of four self-paced household and garden tasks to determine whether 55- to 65-year-old men performed them at a moderate intensity [3-6 metabolic equivalents (METs)] and to predict the activity intensity via indirect methods. Resting metabolic rate and oxygen consumption were measured using Douglas bags in 50 men (X +/- SD: 60.6 +/-3.2 years, 175.8 +/- 5.6 cm, 82.6 +/- 10.1 kg ) who performed self-perceived moderate paced walking and self-paced sweeping, window cleaning, vacuuming and lawn mowing. Heart rate, CSA accelerometer counts (hip and arm), Quetelet's index, Borg rating of perceived exertion and respiratory frequency were measured as possible predictors of energy expenditure. Each of the four household and garden activities was performed at a mean intensity of > or = 3.0 METs in both the standardised laboratory environment (sweeping = 3.4, window cleaning = 3.8, vacuuming = 3.0 and lawn mowing = 5.3 METs) and the subjects' homes (sweeping = 4.1, window cleaning = 3.5, vacuuming = 3.6 and lawn mowing = 5.0 METs). Comparisons between the two settings were significantly different (p < or =0.008). Except for window cleaning, the MET values were not different from those of our previous younger sample (35-45 years). Regression analysis yielded prediction equations with 95% confidence intervals of +/-0.8 METs for both the laboratory and home environments. Although the energy expenditure means for these activities indicate that they can contribute to the 30 min day(-1) of moderate intensity physical activity required to confer health benefits, there was substantial inter-individual variability. While the regression equations lack predictive precision at the individual level, they were able to determine whether energy expenditure was above the 3.0 MET threshold with correct classification rates of 91% and 94% in the laboratory and home, respectively.

A comparison of spinergy versus standard steel-spoke wheelchair wheels

OBJECTIVES

To compare the energy efficiency of straight-line wheeling using Spinergy wheels as compared with standard steel-spoke wheels, and to assess the 2 wheels in terms of user comfort and wheeling preference during a wheeling course with multiple turns and surfaces.

DESIGN

Nonblinded randomized crossover trial.

SETTING

Rehabilitation center.

PARTICIPANTS

Twenty persons with paraplegia (neurologic level T6 and below).

INTERVENTION

Wheeling a straight line and obstacle course with Spinergy or standard spoke wheelchair wheels.

MAIN OUTCOME MEASURES

Velocity and Physiological Cost Index (PCI) while wheeling over ground at a self-selected pace, and the User Preference Questionnaire after wheeling an obstacle course, using Spinergy or standard spoke wheelchair wheels.

RESULTS

There was no significant difference in wheeling energy efficiency between the Spinergy and the steel-spoke wheels as measured by PCI ( P =.975). When rated for overall comfort, the Spinergy wheels were preferred over steel-spoke wheels ( P =.002).

CONCLUSIONS

Spinergy wheels provided a more comfortable ride, but did not differ from standard steel-spoked wheels in terms of energy efficiency. The increased comfort may have important implications in patient management of pain and spasticity.

Improving energy expenditure estimation for physical activity

PURPOSE

The purpose of this study was to validate the Intelligent Device for Energy Expenditure and Activity (IDEEA) for estimation of energy expenditure during a variety of activities. An additional aim was to improve the accuracy of the estimation of energy expenditure of physical activity based on second-by-second information of type, onset, and duration of activity.

METHODS

This study included two tests: a mask calorimetry test with 27 subjects [age = 33.7 +/- 13.8 (mean +/- SD) yr; BMI = 24.8 +/- 4.8 kg x m] and a respiratory chamber calorimetry test with 10 subjects (age = 32.9 +/- 12.4 yr; BMI = 26.1 +/- 5.6 kg x m). In the mask test, the subjects performed activities (sitting, standing, lying down, level treadmill walking, and running at different speeds) for 50-min durations. For the chamber test, subjects lived in the metabolic chamber for 23 h and performed three exercise sessions to compensate for the confined environment.

RESULTS

The results showed significant correlations (P < 0.0001) between energy expenditure estimated by IDEEA and energy expenditure measured by the calorimeters with an accuracy >95%. After corrections for the decrease in sleeping metabolic rate, the estimation accuracy for the chamber test was increased by 1-96.2%, whereas the estimation accuracy for nighttime activity was significantly improved by 4-99%.

CONCLUSION

IDEEA provides a suitable method for estimating the energy expenditure of physical activity. It provides both instantaneous and cumulative estimates of energy expenditure over a given period.

Measurement and prediction of energy expenditure in males during household and garden tasks

Participation in at least 30 min of moderate intensity activity on most days is assumed to confer health benefits. This study accordingly determined whether the more vigorous household and garden tasks (sweeping, window cleaning, vacuuming and lawn mowing) are performed by middle-aged men at a moderate intensity of 3-6 metabolic equivalents (METs) in the laboratory and at home. Measured energy expenditure during self-perceived moderate-paced walking was used as a marker of exercise intensity. Energy expenditure was also predicted via indirect methods. Thirty-six males [ X (SD): 40.0 (3.3) years; 179.5 (6.9) cm; 83.4 (14.0) kg] were measured for resting metabolic rate (RMR) and oxygen consumption ( VO(2)) during the five activities using the Douglas bag method. Heart rate, respiratory frequency, CSA (Computer Science Applications) movement counts, Borg scale ratings of perceived exertion and Quetelet's index were also recorded as potential predictors of exercise intensity. Except for vacuuming in the laboratory, which was not significantly different from 3.0 METs ( P=0.98), the MET means in the laboratory and home were all significantly greater than 3.0 ( P</=0.006). The sweeping and vacuuming MET means were significantly higher ( P<0.001) at home than in the laboratory, whereas the converse applied for window cleaning and lawn mowing. Measured RMR was significantly lower ( P<0.001) than the 1-MET constant. Estimating METs by fitting random intercept regression models to the data resulted in standard deviations for the "leave-one-out" prediction errors (predicted-measured) of 0.4 and 0.5 METs for the laboratory and home equations, respectively. While the means indicate that all the activities were performed at a moderate intensity, there was great inter-individual variability in energy expenditure. The laboratory and home-based equations predicted with correct classification rates of 89% and 88%, respectively, whether energy expenditure was <3.0 or >/=3.0 METs.

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.

Heart rate monitoring: applications and limitations

Over the last 20 years, heart rate monitors (HRMs) have become a widely used training aid for a variety of sports. The development of new HRMs has also evolved rapidly during the last two decades. In addition to heart rate (HR) responses to exercise, research has recently focused more on heart rate variability (HRV). Increased HRV has been associated with lower mortality rate and is affected by both age and sex. During graded exercise, the majority of studies show that HRV decreases progressively up to moderate intensities, after which it stabilises. There is abundant evidence from cross-sectional studies that trained individuals have higher HRV than untrained individuals. The results from longitudinal studies are equivocal, with some showing increased HRV after training but an equal number of studies showing no differences. The duration of the training programmes might be one of the factors responsible for the versatility of the results.HRMs are mainly used to determine the exercise intensity of a training session or race. Compared with other indications of exercise intensity, HR is easy to monitor, is relatively cheap and can be used in most situations. In addition, HR and HRV could potentially play a role in the prevention and detection of overtraining. The effects of overreaching on submaximal HR are controversial, with some studies showing decreased rates and others no difference. Maximal HR appears to be decreased in almost all 'overreaching' studies. So far, only few studies have investigated HRV changes after a period of intensified training and no firm conclusions can be drawn from these results. The relationship between HR and oxygen uptake (VO(2)) has been used to predict maximal oxygen uptake (VO(2max)). This method relies upon several assumptions and it has been shown that the results can deviate up to 20% from the true value. The HR-VO(2) relationship is also used to estimate energy expenditure during field conditions. There appears to be general consensus that this method provides a satisfactory estimate of energy expenditure on a group level, but is not very accurate for individual estimations. The relationship between HR and other parameters used to predict and monitor an individual's training status can be influenced by numerous factors. There appears to be a small day-to-day variability in HR and a steady increase during exercise has been observed in most studies. Furthermore, factors such as dehydration and ambient temperature can have a profound effect on the HR-VO(2) relationship.

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

PURPOSE

To determine the validity of the simultaneous heart rate-motion sensor (HR+M) technique for estimating energy expenditure (EE) by comparing it with indirect calorimetry. In addition, we examined the validity of the flex heart rate (FlexHR) method to estimate EE.

METHODS

Ten participants (4 men: 26.7 yr +/- 1.5, and 6 women: 26.5 yr +/-3.3) performed arm and leg work in the laboratory for the purpose of developing individualized HR-oxygen uptake (VO2) regression equations. Participants completed physical tasks in a field setting while HR, VO2, and motion sensor data were collected on a near-continuous basis for 6 h. Accelerometers, one on the arm and one on the leg, were used to discriminate between upper- and lower-body movement. HR was used to predict EE (METs) from the corresponding laboratory regression equation. Predicted values (METs) were compared with measured values (METs) obtained via a portable metabolic measurement system.

RESULTS

The simultaneous HR+M technique showed a significantly stronger relationship with VO2 (R2 = 0.81, SEE = 0.55 METs) in comparison with the FlexHR method (R2 = 0.63, SEE = 0.76 METs) (P < 0.001). The FlexHR method significantly overestimated measured minute-by-minute EE (P < 0.001), whereas the simultaneous HR+M technique did not. The simultaneous HR+M technique accurately reflected time spent in resting/light, moderate, and hard activity, whereas the FlexHR method underpredicted time spent in resting/light activity (P = 0.02) and overpredicted time spent in moderate activity (P = 0.02). The simultaneous HR+M technique also accurately estimated total 6-h EE.

CONCLUSION

The simultaneous HR+M technique is an accurate predictor of EE during free-living activity and provides a valid measure of the time spent in various intensity categories.

Simultaneous heart rate-motion sensor technique to estimate energy expenditure

PURPOSE

Heart rate (HR) and motion sensors represent promising tools for physical activity (PA) assessment, as each provides an estimate of energy expenditure (EE). Although each has inherent limitations, the simultaneous use of HR and motion sensors may increase the accuracy of EE estimates. The primary purpose of this study was to establish the accuracy of predicting EE from the simultaneous HR-motion sensor technique. In addition, the accuracy of EE estimated by the simultaneous HR-motion sensor technique was compared to that of HR and motion sensors used independently.

METHODS

Thirty participants (16 men: age, 33.1 +/- 12.2 yr; BMI, 26.1 +/- 0.7 kg.m(-2); and 14 women: age, 31.9 +/- 13.1 yr; BMI, 27.2 +/- 1.1 kg.m(-2) (mean +/- SD)) performed arm and leg work in the laboratory for the purpose of developing individualized HR-VO2 regression equations. Participants then performed physical tasks in a field setting for 15 min each. CSA accelerometers placed on the arm and leg were to discriminate between upper and lower body movement, and HR was then used to predict EE (METs) from the corresponding arm or leg laboratory regression equation. A hip-mounted CSA accelerometer and Yamax pedometer were also used to predict EE. Predicted values (METs) were compared to measured values (METs), obtained via a portable metabolic measurement system (Cosmed K4b(2)).

RESULTS

The Yamax pedometer and the CSA accelerometer on the hip significantly underestimated the energy cost of selected physical activities, whereas HR alone significantly overestimated the energy cost of selected physical activities. The simultaneous HR-motion sensor technique showed the strongest relationship with VO(2) (R(2) = 0.81) and did not significantly over- or underpredict the energy cost (P = 0.341).

CONCLUSION

The simultaneous HR-motion sensor technique is a good predictor of EE during selected lifestyle activities, and allows researchers to more accurately quantify free-living PA.

Estimating energy expenditure by heart-rate monitoring without individual calibration

Heart rate monitoring has been shown to be a valid method for measuring free-living energy expenditure at the group level, but its use in large-scale studies is limited by the need for an individual calibration of the relationship between heart rate and energy expenditure.

PURPOSE

To determine whether energy expenditure can be estimated from heart rate monitoring without individual calibration in epidemiological studies.

METHODS

Our previously validated heart rate monitoring method relies on measuring individual calibration parameters obtained from resting energy expenditure and the regression line between energy expenditure and heart rate during exercise. We developed prediction equations for these parameters using easily measured variables in a population-based study of 789 individuals. The predictive ability of these parameters was tested in a separate population-based sample (N = 97).

RESULTS

Physical activity level (PAL = total energy expenditure/basal metabolic rate) using the four estimated parameters was correlated with PAL using the measured parameters (r = 0.82, P < 0.01). Comparison of measured and estimated PAL showed that 97.9% of the scores were placed in the same or adjacent quartile.

CONCLUSION

A combination of simple measurements and heart rate monitoring produces estimates of energy expenditure that are highly correlated with those obtained using full individual calibration. This simplification of the heart rate monitoring method could extend its use in ranking individuals in epidemiological studies.

Methodologic issues in measuring physical activity and physical fitness when evaluating the role of dietary supplements for physically active people

Physical activity and physical fitness are complex entities comprising numerous diverse components that present a challenge in terms of accurate, reliable measurement. Physical activity can be classified by its mechanical (static or dynamic) or metabolic (aerobic or anaerobic) characteristics and its intensity (absolute or relative to the person's capacity). Habitual physical activity can be assessed by using a variety of questionnaires, diaries, or logs and by monitoring body movement or physiologic responses. Selection of a measurement method depends on the purpose of the evaluation, the nature of the study population, and the resources available. The various components of physical fitness can be assessed accurately in the laboratory and, in many cases, in the field by using a composite of performance tests. Most coaches and high-level athletes would accept as very beneficial a dietary supplement that would increase performance in a competitive event by even 3%; for example, lowering a runner's time of 3 min, 43 s in the 1500 m by 6.7 s. To establish that such small changes are caused by the dietary supplement requires carefully conducted research that involves randomized, placebo-controlled, double-blind studies designed to maximize statistical power. Statistical power can be increased by enlarging sample size, selecting tests with high reliability, selecting a potent but safe supplement, and maximizing adherence. Failure to design studies with adequate statistical power will produce results that are unreliable and will increase the likelihood that a true effect will be missed.