Validity of combining heart rate and uniaxial acceleration to measure free-living physical activity energy expenditure in young men

Detection of sedentary time and bouts using consumer-grade wrist-worn devices: a hidden semi-Markov model

BACKGROUND

Wrist-worn data from commercially available devices has potential to characterize sedentary time for research and for clinical and public health applications. We propose a model that utilizes heart rate in addition to step count data to estimate the proportion of time spent being sedentary and the usual length of sedentary bouts.

METHODS

We developed and trained two Hidden semi-Markov models, STEPHEN (STEP and Heart ENcoder) and STEPCODE (STEP enCODEr; a steps-only based model) using consumer-grade Fitbit device data from participants under free living conditions, and validated model performance using two external datasets. We used the median absolute percentage error (MDAPE) to measure the accuracy of the proposed models against research-grade activPAL device data as the referent. Bland-Altman plots summarized the individual-level agreement with activPAL.

RESULTS

In OPTIMISE cohort, STEPHEN's estimates of the proportion of time spent sedentary had significantly (p < 0.001) better accuracy (MDAPE [IQR] = 0.15 [0.06-0.25] vs. 0.23 [0.13-0.53)]) and agreement (Bias Mean [SD]=-0.03[0.11] vs. 0.14 [0.11]) than the proprietary software, estimated the usual sedentary bout duration more accurately (MDAPE[IQR] = 0.11[0.06-0.26] vs. 0.42[0.32-0.48]), and had better agreement (Bias Mean [SD] = 3.91[5.67] minutes vs. -11.93[5.07] minutes). With the ALLO-Active dataset, STEPHEN and STEPCODE did not improve the estimation of proportion of time spent sedentary, but STEPHEN estimated usual sedentary bout duration more accurately than the proprietary software (MDAPE[IQR] = 0.19[0.03-0.25] vs. 0.36[0.15-0.48]) and had smaller bias (Bias Mean[SD] = 0.70[8.89] minutes vs. -11.35[9.17] minutes).

CONCLUSIONS

STEPHEN can characterize the proportion of time spent being sedentary and usual sedentary bout length. The methodology is available as an open access R package available from https://github.com/limfuxing/stephen/ . The package includes trained models, but users have the flexibility to train their own models.

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.

Development and Calibration of a PATCH Device for Monitoring Children's Heart Rate and Acceleration

INTRODUCTION

Current wearables that collect heart rate and acceleration were not designed for children and/or do not allow access to raw signals, making them fundamentally unverifiable. This study describes the creation and calibration of an open-source multichannel platform (PATCH) designed to measure heart rate and acceleration in children ages 3-8 yr.

METHODS

Children (N = 63; mean age, 6.3 yr) participated in a 45-min protocol ranging in intensities from sedentary to vigorous activity. Actiheart-5 was used as a comparison measure. We calculated mean bias, mean absolute error (MAE) mean absolute percent error (MA%E), Pearson correlations, and Lin's concordance correlation coefficient (CCC).

RESULTS

Mean bias between PATCH and Actiheart heart rate was 2.26 bpm, MAE was 6.67 bpm, and M%E was 5.99%. The correlation between PATCH and Actiheart heart rate was 0.89, and CCC was 0.88. For acceleration, mean bias was 1.16 mg and MAE was 12.24 mg. The correlation between PATCH and Actiheart was 0.96, and CCC was 0.95.

CONCLUSIONS

The PATCH demonstrated clinically acceptable accuracies to measure heart rate and acceleration compared with a research-grade device.

Energy Expenditure of Elite Male and Female Professional Tennis Players During Habitual Training

Understanding the daily energy expenditure of athletes during training is important to support recovery, adaptation, and the maintenance of performance. The aim of the current research was to assess the total daily energy expenditure (TDEE) and the acute energy expenditure (EE) of tennis training sessions during habitual training of elite tennis players. Using a cohort study design, 27 (n = 10, male; age; 22.3 ± 3.2 years and n = 17, female; age: 23.8 ± 3.5 years) elite singles tennis players were assessed for TDEE and tennis training EE. Using Actiheart activity monitors during a 2- to 5-day training period, male players were analyzed for 26 days and 33 (1.3 ± 0.5 sessions/day) tennis training sessions, and female players for 43 days and 58 (1.2 ± 0.4 sessions/day) tennis training sessions. Male TDEE (4,708 ± 583 kcal/day) was significantly higher than female (3,639 ± 305 kcal/day). Male absolute and relative tennis training EEs (10.2 ± 2.3 kcal/min and 7.9 ± 1.4 kcal·hr-1·kg-1) were significantly higher than those of females (7.6 ± 1.0 kcal/min and 6.8 ± 0.9 kcal·hr-1·kg-1). The resting metabolic rate was assessed via indirect calorimetry. The physical activity level for both groups was 2.3 AU. The TDEE of male and female players during habitual training now highlights the continual cycle of high energy demands experienced by the elite tennis player. The broad ranges of TDEE and EE reported here suggest individual assessment and nutritional planning be prioritized, with a particular focus on carbohydrate requirements.

Assessment of activity energy expenditure during competitive golf: The effects of bag carrying, electric or manual trolleys

Golf is a sport played around the globe, with an estimated 42.6 million people playing within the United Kingdom and United States of America alone. To date, there is limited data on the energy expenditure of golf. The present study assessed the activity energy expenditure (AEE) of 16 high-standard (handicap under 5) golfers who completed three rounds of competitive golf either carrying the golf bag (BC), using a manual push trolley (MT) or an electric trolley (ET) (Stewart Golf, Gloucester, UK). Prior to each round, participants were fitted with an Actiheart® accelerometer (Camntech, Fenstanton, UK) to estimate AEE, whilst ratings of perceived exertion (RPE) and enjoyment were collected following each round. Data were analysed using a one-way repeated measures ANOVA, with Hedges g effect sizes (ES) calculated. Mean (SD) AEE was 688 ± 213 kcal for BC, 756 ± 210 kcal for MT and 663 ± 218 kcal for ET (p = .05) although these differences were deemed small or less. The ET condition resulted in the lowest mean heart rate, moderate or very large from BC or MT, respectively. There were no significant differences in enjoyment although perceived exertion was lowest in the ET condition. In summary, we report meaningful differences in AEE between the three conditions (p = .05), with perceived exertion and maximum HR being lowest when using the electric trolley. Golf may be considered as an effective intervention to increase step count and improve physical activity levels across the general population regardless of transportation methods of clubs.

Concurrent validity of the combined HRV/ACC sensor and physical activity diary when monitoring physical activity in university students during free-living days

The purpose of this research was to determine if the scientific research device combined heart rate variability combined with an acceleration sensor (Firstbeat Bodyguard 2, BG2) was valid and reliable for time spent in different intensity zones in free-living. A total of 55 healthy participants performed 48-h physical activity (PA) monitoring with BG2, ActiGraph GT3X+ (GT3X+), and completed Bouchard Physical Activity Diary (Bouchard) every night. In the available studies, GT3X+ is considered the gold standard scientific research device for PA monitor. We compared BG2 and Bouchard with GT3X+ by difference, correlation, and agreement of PA and energy expenditure (EE) in free-living. The results showed that BG2 estimated PA more accurately than Bouchard, with a modest correlation (r > 0.49), strong agreement (τ > 0.29), and they had the lowest limits of agreement when estimating moderate to vigorous physical activity (MVPA). The EE estimated by Bouchard was the highest among the three methods, and the correlation and agreement between the three methods were high. Our findings showed that the BG2 is valid and reliable for estimating time spent in different intensity zones in free-living, especially in MVPA.

Quality Evaluation of Free-living Validation Studies for the Assessment of 24-Hour Physical Behavior in Adults via Wearables: Systematic Review

Background

Wearable technology is a leading fitness trend in the growing commercial industry and an established method for collecting 24-hour physical behavior data in research studies. High-quality free-living validation studies are required to enable both researchers and consumers to make guided decisions on which study to rely on and which device to use. However, reviews focusing on the quality of free-living validation studies in adults are lacking.

Objective

This study aimed to raise researchers’ and consumers’ attention to the quality of published validation protocols while aiming to identify and compare specific consistencies or inconsistencies between protocols. We aimed to provide a comprehensive and historical overview of which wearable devices have been validated for which purpose and whether they show promise for use in further studies.

Methods

Peer-reviewed validation studies from electronic databases, as well as backward and forward citation searches (1970 to July 2021), with the following, required indicators were included: protocol must include real-life conditions, outcome must belong to one dimension of the 24-hour physical behavior construct (intensity, posture or activity type, and biological state), the protocol must include a criterion measure, and study results must be published in English-language journals. The risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool with 9 questions separated into 4 domains (patient selection or study design, index measure, criterion measure, and flow and time).

Results

Of the 13,285 unique search results, 222 (1.67%) articles were included. Most studies (153/237, 64.6%) validated an intensity measure outcome such as energy expenditure. However, only 19.8% (47/237) validated biological state and 15.6% (37/237) validated posture or activity-type outcomes. Across all studies, 163 different wearables were identified. Of these, 58.9% (96/163) were validated only once. ActiGraph GT3X/GT3X+ (36/163, 22.1%), Fitbit Flex (20/163, 12.3%), and ActivPAL (12/163, 7.4%) were used most often in the included studies. The percentage of participants meeting the quality criteria ranged from 38.8% (92/237) to 92.4% (219/237). On the basis of our classification tree to evaluate the overall study quality, 4.6% (11/237) of studies were classified as low risk. Furthermore, 16% (38/237) of studies were classified as having some concerns, and 72.9% (173/237) of studies were classified as high risk.

Conclusions

Overall, free-living validation studies of wearables are characterized by low methodological quality, large variability in design, and focus on intensity. Future research should strongly aim at biological state and posture or activity outcomes and strive for standardized protocols embedded in a validation framework. Standardized protocols for free-living validation embedded in a framework are urgently needed to inform and guide stakeholders (eg, manufacturers, scientists, and consumers) in selecting wearables for self-tracking purposes, applying wearables in health studies, and fostering innovation to achieve improved validity.

The effects of exercise training on vascular function among overweight adults with obstructive sleep apnea

Obstructive sleep apnea (OSA) is associated with increased cardiovascular morbidity due, in part, to impaired vascular function. Exercise confers cardioprotection by improving vascular health. Yet, whether OSA severity affects the vascular improvements conferred with exercise training is not known. Overweight (body mass index (BMI) >27 kg/m²) adults were evaluated for OSA and enrolled in a six-week exercise intervention. Baseline assessments of brachial artery flow-mediated dilation (BAFMD), central augmentation index (AIx) and pulse wave velocity (PWV) were repeated post training. Fifty-one participants (25 men; 26 women) completed the study. Despite improved aerobic capacity (p=0.0005) and total fat mass (p=0.0005), no change in vascular function was observed. Participants were divided into two severity groups according to their baseline total apnea-hypopnea index (AHI) as either 5 to 14.9 events•hr^-1 (n= 21; Age=48 ± 7 yrs; BMI=33.7 ± 4.6kg•m^-2) or 15 ≥events•hr^-1 (n=30; Age=56 ± 13 yrs; BMI = 34.3 ± 4.2 kg•m^-2). No effect of OSA group was observed for BAFMD (p=0.82), AIx (p=0.37) or PWV (p=0.44), suggesting that OSA severity does not influence the effect of exercise on vascular function. The vascular effects of extended exercise programs of greater intensity in overweight OSA patients should be examined.

Modeling Energy Expenditure Estimation in Occupational Context by Actigraphy: A Multi Regression Mixed-Effects Model

The accurate prediction of energy requirements for healthy individuals has many useful applications. The occupational perspective has also been proven to be of great utility for improving workers' ergonomics, safety, and health. This work proposes a statistical regression model based on actigraphy and personal characteristics to estimate energy expenditure and cross-validate the results with reference standardized methods. The model was developed by hierarchical mixed-effects regression modeling based on the multitask protocol data. Measurements combined actigraphy, indirect calorimetry, and other personal and lifestyle information from healthy individuals (n = 50) within the age of 29.8 ± 5 years old. Results showed a significant influence of the variables related to movements, heart rate and anthropometric variables of body composition for energy expenditure estimation. Overall, the proposed model showed good agreement with energy expenditure measured by indirect calorimetry and evidenced a better performance than the methods presented in the international guidelines for metabolic rate assessment proving to be a reliable alternative to normative guidelines. Furthermore, a statistically significant relationship was found between daily activity and energy expenditure, which raised the possibility of further studies including other variables, namely those related to the subject's lifestyle.

Bidirectional Associations of Momentary Affect with Physical Activity and Sedentary Behaviors in Working Adults

BACKGROUND

Affective experiences and movement-based behaviors form a system that has been shown to influence exercise adherence and mental health outcomes. Little is known about the naturalistic dynamics of the reciprocal associations in this system.

PURPOSE

We examined the time intervals at which momentary affect precedes and follows movement-based behaviors in everyday life.

METHODS

A community sample of working adults (n = 111) completed ecological momentary assessments (EMA) asking about current affect states (sad, happy, tired, and interested) six times a day for three consecutive days. Ratings were used to generate scores for momentary affective arousal and valence. Participants also wore an activity monitor. Total activity counts and sedentary duration in the shorter to longer time intervals (5-120 min) before or after EMA were used as indicators of movement-based behaviors.

RESULTS

Multilevel modeling showed that current affective arousal predicted higher subsequent activity counts in the longer time intervals (120 min) and less subsequent sedentary behavior in the shorter to longer time intervals (5, 60, and 120 min). For the reversed sequence, neither movement-based behavior predicted subsequent momentary arousal or valence. Affective valence was unrelated to movement-based behaviors in either temporal direction.

CONCLUSIONS

Some naturally occurring affective experiences (i.e., arousal) might precede, rather than follow, movement-based behaviors. Understanding affective arousal may contribute to improved management of subsequent movement-based behaviors in everyday life.

Animal board invited review: OneARK: Strengthening the links between animal production science and animal ecology

Wild and farmed animals are key elements of natural and managed ecosystems that deliver functions such as pollination, pest control and nutrient cycling within the broader roles they play in contributing to biodiversity and to every category of ecosystem services. They are subjected to global changes with a profound impact on the natural range and viability of animal species, the emergence and spatial distribution of pathogens, land use, ecosystem services and farming sustainability. We urgently need to improve our understanding of how animal populations can respond adaptively and therefore sustainably to these new selective pressures. In this context, we explored the common points between animal production science and animal ecology to identify promising avenues of synergy between communities through the transfer of concepts and/or methodologies, focusing on seven concepts that link both disciplines. Animal adaptability, animal diversity (both within and between species), selection, animal management, animal monitoring, agroecology and viability risks were identified as key concepts that should serve the cross-fertilization of both fields to improve ecosystem resilience and farming sustainability. The need for breaking down interdisciplinary barriers is illustrated by two representative examples: i) the circulation and reassortment of pathogens between wild and domestic animals and ii) the role of animals in nutrient cycles, i.e. recycling nitrogen, phosphorus and carbon through, for example, contribution to soil fertility and carbon sequestration. Our synthesis identifies the need for knowledge integration techniques supported by programmes and policy tools that reverse the fragmentation of animal research toward a unification into a single Animal Research Kinship, OneARK, which sets new objectives for future science policy. At the interface of animal ecology and animal production science, our article promotes an effective application of the agroecology concept to animals and the use of functional diversity to increase resilience in both wild and farmed systems. It also promotes the use of novel monitoring technologies to quantify animal welfare and factors affecting fitness. These measures are needed to evaluate viability risk, predict and potentially increase animal adaptability and improve the management of wild and farmed systems, thereby responding to an increasing demand of society for the development of a sustainable management of systems.

Feasibility and Validity of the Actiheart Activity Monitor in Children Who Were Hospitalized With Cancer Coadmitted With Classmates: A RESPECT Study

PURPOSE

We investigated the feasibility of the Actiheart monitor to determine total daily energy expenditure and the validity of the Actiheart step test as an accurate estimate of peak oxygen uptake.

METHODS

(Equation is included in full-text article.)O2 peak was estimated with the Actiheart step test and compared with a cardiopulmonary exercise test. Total daily energy expenditure was measured using the Actiheart monitor on days with and without classmate coadmission.

RESULTS

Of 26 eligible measurement periods (15 children), 89% participated and 91% could participate safely; however, 35% fulfilled demands for valid monitoring. The percentage of children not completing the monitoring period was 10% (attrition) and adherence to classmate visits was 84%. Forty-eight percent of the measurement periods provided data, and only 27% was calibrated data. Actiheart step test significantly overestimated (Equation is included in full-text article.)O2 peak compared with the Cardio Pulmonary Exercise Test.

CONCLUSION

Measuring total daily energy expenditure using Actiheart is not feasible, nor implementable in children with cancer. Furthermore, the Actiheart step test is not a valid test to estimate (Equation is included in full-text article.)O2 peak in children with cancer.

Validity of Wrist-Worn photoplethysmography devices to measure heart rate: A systematic review and meta-analysis

Heart rate (HR), when combined with accelerometry, can dramatically improve estimates of energy expenditure and sleep. Advancements in technology, via the development and introduction of small, low-cost photoplethysmography devices embedded within wrist-worn consumer wearables, have made the collection of heart rate (HR) under free-living conditions more feasible. This systematic review and meta-analysis compared the validity of wrist-worn HR estimates to a criterion measure of HR (electrocardiography ECG or chest strap). Searches of PubMed/Medline, Web of Science, EBSCOhost, PsycINFO, and EMBASE resulted in a total of 44 articles representing 738 effect sizes across 15 different brands. Multi-level random effects meta-analyses resulted in a small mean difference (beats per min, bpm) of -0.40 bpm (95 confidence interval (CI) -1.64 to 0.83) during sleep, -0.01 bpm (-0.02 to 0.00) during rest, -0.51 bpm (-1.60 to 0.58) during treadmill activities (walking to running), while the mean difference was larger during resistance training (-7.26 bpm, -10.46 to -4.07) and cycling (-4.55 bpm, -7.24 to -1.87). Mean difference increased by 3 bpm (2.5 to 3.5) per 10 bpm increase of HR for resistance training. Wrist-worn devices that measure HR demonstrate acceptable validity compared to a criterion measure of HR for most common activities.

Lipid Metabolism Links Nutrient-Exercise Timing to Insulin Sensitivity in Men Classified as Overweight or Obese

CONTEXT

Pre-exercise nutrient availability alters acute metabolic responses to exercise, which could modulate training responsiveness.

OBJECTIVE

To assess acute and chronic effects of exercise performed before versus after nutrient ingestion on whole-body and intramuscular lipid utilization and postprandial glucose metabolism.

DESIGN

(1) Acute, randomized, crossover design (Acute Study); (2) 6-week, randomized, controlled design (Training Study).

SETTING

General community.

PARTICIPANTS

Men with overweight/obesity (mean ± standard deviation, body mass index: 30.2 ± 3.5 kg⋅m-2 for Acute Study, 30.9 ± 4.5 kg⋅m-2 for Training Study).

INTERVENTIONS

Moderate-intensity cycling performed before versus after mixed-macronutrient breakfast (Acute Study) or carbohydrate (Training Study) ingestion.

RESULTS

Acute Study-exercise before versus after breakfast consumption increased net intramuscular lipid utilization in type I (net change: -3.44 ± 2.63% versus 1.44 ± 4.18% area lipid staining, P < 0.01) and type II fibers (-1.89 ± 2.48% versus 1.83 ± 1.92% area lipid staining, P < 0.05). Training Study-postprandial glycemia was not differentially affected by 6 weeks of exercise training performed before versus after carbohydrate intake (P > 0.05). However, postprandial insulinemia was reduced with exercise training performed before but not after carbohydrate ingestion (P = 0.03). This resulted in increased oral glucose insulin sensitivity (25 ± 38 vs -21 ± 32 mL⋅min-1⋅m-2; P = 0.01), associated with increased lipid utilization during exercise (r = 0.50, P = 0.02). Regular exercise before nutrient provision also augmented remodeling of skeletal muscle phospholipids and protein content of the glucose transport protein GLUT4 (P < 0.05).

CONCLUSIONS

Experiments investigating exercise training and metabolic health should consider nutrient-exercise timing, and exercise performed before versus after nutrient intake (ie, in the fasted state) may exert beneficial effects on lipid utilization and reduce postprandial insulinemia.

Everyday stress components and physical activity: examining reactivity, recovery and pileup

The experience of naturally-occurring stress in daily life has been linked with lower physical activity levels. However, most of this evidence comes from general and static reports of stress. Less is known how different temporal components of everyday stress interfere with physical activity. In a coordinated secondary analysis of data from two studies of adults, we used intensive, micro-longitudinal assessments (ecological momentary assessments, EMA) to investigate how distinct components of everyday stress, that is, reactivity to stressor events, recovery from stressor events, and pileup of stressor events and responses predict physical activity. Results showed that components of everyday stress predicted subsequent physical activity especially for indicators of stress pileup. In both studies, the accumulation of stress responses over the previous 12 h was more predictive of subsequent physical activity than current stress reactivity or recovery responses. Results are compared to the effects of general measures of perceived stress that showed an opposite pattern of results. The novel everyday stress approach used here may be fruitful for generating new insights into physical activity specifically and health behaviors in general.

Habitual physical activity is associated with lower fasting and greater glucose-induced GLP-1 response in men

RATIONALE

The hormone glucagon-like peptide-1 (GLP-1) decreases blood glucose and appetite. Greater physical activity (PA) is associated with lower incidence of type 2 diabetes. While acute exercise may increase glucose-induced response of GLP-1, it is unknown how habitual PA affects GLP-1 secretion. We hypothesised that habitual PA associates with greater glucose-induced GLP-1 responses in overweight individuals.

METHODS

Cross-sectional analysis of habitual PA levels and GLP-1 concentrations in 1326 individuals (mean (s.d.) age 66 (7) years, BMI 27.1 (4.5) kg/m2) from the ADDITION-PRO cohort. Fasting and oral glucose-stimulated GLP-1 responses were measured using validated radioimmunoassay. PA was measured using 7-day combined accelerometry and heart rate monitoring. From this, energy expenditure (PAEE; kJ/kg/day) and fractions of time spent in activity intensities (h/day) were calculated. Cardiorespiratory fitness (CRF; mL O2/kg/min) was calculated using step tests. Age-, BMI- and insulin sensitivity-adjusted associations between PA and GLP-1, stratified by sex, were evaluated by linear regression analysis.

RESULTS

In 703 men, fasting GLP-1 concentrations were 20% lower (95% CI: -33; -3%, P = 0.02) for every hour of moderate-intensity PA performed. Higher CRF and PAEE were associated with 1-2% lower fasting GLP-1 (P = 0.01). For every hour of moderate-intensity PA, the glucose-stimulated GLP-1 response was 16% greater at peak 30 min (1; 33%, P rAUC0-30 = 0.04) and 20% greater at full response (3; 40%, P rAUC0-120 = 0.02). No associations were found in women who performed PA 22 min/day vs 32 min/day for men.

CONCLUSION

Moderate-intensity PA is associated with lower fasting and greater glucose-induced GLP-1 responses in overweight men, possibly contributing to improved glucose and appetite regulation with increased habitual PA.

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.

Correction of estimation bias of predictive equations of energy expenditure based on wrist/waist-mounted accelerometers

Background

Using wearable inertial sensors to accurately estimate energy expenditure (EE) during an athletic training process is important. Due to the characteristics of inertial sensors, however, the positions in which they are worn can produce signals of different natures. To understand and solve this issue, this study used the heart rate reserve (HRR) as a compensation factor to modify the traditional empirical equation of the accelerometer EE sensor and examine the possibility of improving the estimation of energy expenditure for sensors worn in different positions.

Methods

Indirect calorimetry was used as the criterion measure (CM) to measure the EE of 90 healthy adults on a treadmill (five speeds: 4.8, 6.4, 8.0, 9.7, and 11.3 km/h). The measurement was simultaneously performed with the ActiGraph GT9X-Link (placed on the wrist and waist) with the Polar H10 Heart Rate Monitor.

Results

At the same exercise intensity, the EE measurements of the GT9X on the wrist and waist had significant differences from those of the CM (p < 0.05). By using multiple regression analysis—utilizing values from vector magnitudes (VM), body weight (BW) and HRR parameters—accuracy of EE estimation was greatly improved compared to traditional equation. Modified models explained a greater proportion of variance (R²) (wrist: 0.802; waist: 0.805) and demonstrated a good ICC (wrist: 0.863, waist: 0.889) compared to Freedson’s VM3 Combination equation (R²: wrist: 0.384, waist: 0.783; ICC: wrist: 0.073, waist: 0.868).

Conclusions

The EE estimation equation combining the VM of accelerometer measurements, BW and HRR greatly enhanced the accuracy of EE estimation based on data from accelerometers worn in different positions, particularly from those on the wrist.

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.

Estimating energy expenditure from wrist and thigh accelerometry in free-living adults: a doubly labelled water study

BACKGROUND

Many large studies have implemented wrist or thigh accelerometry to capture physical activity, but the accuracy of these measurements to infer activity energy expenditure (AEE) and consequently total energy expenditure (TEE) has not been demonstrated. The purpose of this study was to assess the validity of acceleration intensity at wrist and thigh sites as estimates of AEE and TEE under free-living conditions using a gold-standard criterion.

METHODS

Measurements for 193 UK adults (105 men, 88 women, aged 40-66 years, BMI 20.4-36.6 kg m-2) were collected with triaxial accelerometers worn on the dominant wrist, non-dominant wrist and thigh in free-living conditions for 9-14 days. In a subsample (50 men, 50 women) TEE was simultaneously assessed with doubly labelled water (DLW). AEE was estimated from non-dominant wrist using an established estimation model, and novel models were derived for dominant wrist and thigh in the non-DLW subsample. Agreement with both AEE and TEE from DLW was evaluated by mean bias, root mean squared error (RMSE), and Pearson correlation.

RESULTS

Mean TEE and AEE derived from DLW were 11.6 (2.3) MJ day-1 and 49.8 (16.3) kJ day-1 kg-1. Dominant and non-dominant wrist acceleration were highly correlated in free-living (r = 0.93), but less so with thigh (r = 0.73 and 0.66, respectively). Estimates of AEE were 48.6 (11.8) kJ day-1 kg-1 from dominant wrist, 48.6 (12.3) from non-dominant wrist, and 46.0 (10.1) from thigh; these agreed strongly with AEE (RMSE ~12.2 kJ day-1 kg-1, r ~ 0.71) with small mean biases at the population level (~6%). Only the thigh estimate was statistically significantly different from the criterion. When combining these AEE estimates with estimated REE, agreement was stronger with the criterion (RMSE ~1.0 MJ day-1, r ~ 0.90).

CONCLUSIONS

In UK adults, acceleration measured at either wrist or thigh can be used to estimate population levels of AEE and TEE in free-living conditions with high precision.

Skipping Breakfast Before Exercise Creates a More Negative 24-hour Energy Balance: A Randomized Controlled Trial in Healthy Physically Active Young Men

BACKGROUND

At rest, omission of breakfast lowers daily energy intake, but also lowers energy expenditure, attenuating any effect on energy balance. The effect of breakfast omission on energy balance when exercise is prescribed is unclear.

OBJECTIVES

The aim of this study was to assess the effect on 24-h energy balance of omitting compared with consuming breakfast prior to exercise.

METHODS

Twelve healthy physically active young men (age 23 ± 3 y, body mass index 23.6 ± 2.0 kg/m2) completed 3 trials in a randomized order (separated by >1 week): a breakfast of oats and milk (431 kcal; 65 g carbohydrate, 11 g fat, 19 g protein) followed by rest (BR); breakfast before exercise (BE; 60 min cycling at 50 % peak power output); and overnight fasting before exercise (FE). The 24-h energy intake was calculated based on the food consumed for breakfast, followed by an ad libitum lunch, snacks, and dinner. Indirect calorimetry with heart-rate accelerometry was used to measure substrate utilization and 24-h energy expenditure. A [6,6-2H2]glucose infusion was used to investigate tissue-specific carbohydrate utilization.

RESULTS

The 24-h energy balance was -400 kcal (normalized 95% CI: -230, -571 kcal) for the FE trial; this was significantly lower than both the BR trial (492 kcal; normalized 95% CI: 332, 652 kcal) and the BE trial (7 kcal; normalized 95% CI: -153, 177 kcal; both P < 0.01 compared with FE). Plasma glucose utilization in FE (mainly representing liver glucose utilization) was positively correlated with energy intake compensation at lunch (r = 0.62, P = 0.03), suggesting liver carbohydrate plays a role in postexercise energy-balance regulation.

CONCLUSIONS

Neither exercise energy expenditure nor restricted energy intake via breakfast omission were completely compensated for postexercise. In healthy men, pre-exercise breakfast omission creates a more negative daily energy balance and could therefore be a useful strategy to induce a short-term energy deficit. This trial was registered at clinicaltrials.gov as NCT02258399.

Training-induced changes in daily energy expenditure: Methodological evaluation using wrist-worn accelerometer, heart rate monitor, and doubly labeled water technique

INTRODUCTION

Wrist-mounted motion sensors can quantify the volume and intensity of physical activities, but little is known about their long-term validity. Our aim was to validate a wrist motion sensor in estimating daily energy expenditure, including any change induced by long-term participation in endurance and strength training. Supplemental heart rate monitoring during weekly exercise was also investigated.

METHODS

A 13-day doubly labeled water (DLW) measurement of total energy expenditure (TEE) was performed twice in healthy male subjects: during two last weeks of a 12-week Control period (n = 15) and during two last weeks of a 12-week combined strength and aerobic Training period (n = 13). Resting energy expenditure was estimated using two equations: one with body weight and age, and another one with fat-free mass. TEE and activity induced energy expenditure (AEE) were determined from motion sensor alone, and from motions sensor combined with heart rate monitor, the latter being worn during exercise only.

RESULTS

When body weight and age were used in the calculation of resting energy expenditure, the motion sensor data alone explained 78% and 62% of the variation in TEE assessed by DLW at the end of Control and Training periods, respectively, with a bias of +1.75 (p <.001) and +1.19 MJ/day (p = .002). When exercise heart rate data was added to the model, the combined wearable device approach explained 85% and 70% of the variation in TEE assessed by DLW with a bias of +1.89 and +1.75 MJ/day (p <.001 for both). While significant increases in TEE and AEE were detected by all methods as a result of participation in regular training, motion sensor approach underestimated the change measured by DLW: +1.13±0.66 by DLW, +0.59±0.69 (p = .004) by motion sensor, and +0.98±0.70 MJ/day by combination of motion sensor and heart rate. Use of fat-free mass in the estimation of resting energy expenditure removed the biases between the wearable device estimations and the golden standard reference method of TEE and demonstrated a training-induced increase in resting energy expenditure by +0.18±0.13 MJ/day (p <.001).

CONCLUSIONS

Wrist motion sensor combined with a heart rate monitor during exercise sessions, showed high agreement with the golden standard measurement of daily TEE and its change induced by participation in a long-term training protocol. The positive findings concerning the validity, especially the ability to follow-up the change associated with a lifestyle modification, can be considered significant because they partially determine the feasibility of wearable devices as quantifiers of health-related behavior.

Impact of sit-stand desks at work on energy expenditure, sitting time and cardio-metabolic risk factors: Multiphase feasibility study with randomised controlled component

Uncertainties remain about the overall effect of sit-stand desks for reducing prolonged sitting among office-based workers. This study assessed the feasibility of a randomised controlled trial of the impact of workplace sit-stand desks on overall energy expenditure, sitting time and cardio-metabolic outcomes. It involved four phases: Phase I: online survey; Phase II: workspace auditing; Phase III: randomised intervention (provision of sit-stand desks at work for 3 months); Phase IV: qualitative component. Participants were offıce-based employees of two companies in Cambridge, England. Among Phase I participants interested in the trial, 100 were randomised to Phase II. Of those with workspaces suitable for sit-stand desks, 20 were randomised to Phase III. Those allocated to the intervention completed Phase IV. Outcomes included: trial participation interest, desk-type (full desks/desk mounts) and assessment location (work/laboratory/home) preferences (Phase I); proportion of workspaces permitting sit-stand desk installation (Phase II); energy expenditure, sitting time and cardio-metabolic outcomes (Phase III); study participation experiences (Phase IV). Data were collected between May 2015 and December 2016. Recruitment and trial implementation were feasible: 92% of survey respondents expressed participation interest; 80% of workspaces could accommodate sit-stand desks; assessments were done in workplaces, preferred by 71%. Sit-stand desk provision reduced workplace sitting time by 94 min/day (95% CI 17.7-170.7). Their impact on energy expenditure and cardio-metabolic outcomes is unclear. The results confirm the feasibility of a trial assessing sit-stand desks' impact on energy expenditure, sitting time and cardio-metabolic outcomes, which should reduce uncertainty concerning the intervention's potential to reduce the health risks of prolonged sitting. Trial registration ISRCTN44827407.

Comparison of Conventional and Individualized 1-MET Values for Expressing Maximum Aerobic Metabolic Rate and Habitual Activity Related Energy Expenditure

The maximum aerobic metabolic rate can be expressed in multiple metabolically equivalent tasks (MET), i.e., METmax. The purpose was to quantify the error when the conventional (3.5 mL∙kg⁻¹∙min⁻¹) compared to an individualized 1-MET-value is used for calculating METmax and estimating activity energy expenditure (AEE) in endurance-trained athletes (END) and active healthy controls (CON). The resting metabolic rate (RMR, indirect calorimetry) and aerobic metabolic capacity (spiroergometry) were assessed in 52 END (46% male, 27.9 ± 5.7 years) and 53 CON (45% male, 27.3 ± 4.6 years). METmax was calculated as the ratio of VO₂max over VO₂ during RMR (METmax_ind), and VO₂max over the conventional 1-MET-value (METmax_fix). AEE was estimated by multiplying published MET values with the individual and conventional 1-MET-values. Dependent t-tests were used to compare the different modes for calculating METmax and AEE (α = 0.05). In women and men CON, men END METmax_fix was significantly higher than METmax_ind (p < 0.01), whereas, in women END, no difference was found (p > 0.05). The conventional 1-MET-value significantly underestimated AEE in men and women CON, and men END (p < 0.05), but not in women END (p > 0.05). The conventional 1-MET-value appears inappropriate for determining the aerobic metabolic capacity and AEE in active and endurance-trained persons.

Objectively Measured Physical Activity and Falls in Well-Functioning Older Adults: Findings From the Baltimore Longitudinal Study of Aging

OBJECTIVE

Previous work demonstrates the consequences of falling in older adults and the potential of physical activity (PA) to reduce falls, but few studies have used accelerometer-measured PA to compare overall and time-of-day activity patterns of nonfallers, fallers, or subgroups of fallers.

METHODS

In 840 participants (mean age, 66.7; s = 13.2; range, 26-97) of the Baltimore Longitudinal Study of Aging between 2007 and 2014, PA was measured objectively with Actiheart accelerometers and falling status (faller/nonfaller) was assessed during an in-person interview. Differences in daily PA and PA by time-of-day were assessed using multiple linear regression. Differences in PA (multiple linear regression), and functional status (χ) were further examined in subgroups of "risky" or "normal" fallers.

RESULTS

Overall, fallers and nonfallers exhibited similar daily (β = 22.6, P = 0.48) and time-specific PA; however, those who fell doing risky activities were more active overall (β = 243.8, P = 0.002), during the morning (β = 77.3, P = 0.004), afternoon (β = 78.4, P = 0.001), and late afternoon/evening (β = 56.3, P = 0.006) than those who fell doing normal activities. Risky fallers were significantly higher functioning than normal fallers.

CONCLUSIONS

Persons who fell while engaging in normal activities exhibited lower PA overall and throughout most of the day, and were of lower functional status than persons who fell while engaging in risky or unusual activities, suggesting that engagement in risky or unusual PA is associated with higher functional ability and lower falls risk in older persons.

Accuracy of the energy expenditure during uphill exercise measured by the Waist-worn ActiGraph

Background/objective

The application of Micro-Electro-Mechanical Sensors (MEMS) as measurements of energy expenditure (EE) has certain disadvantages. For example, the inertial sensors cannot easily distinguish changes in ground slope during walking/running conditions, so the accuracy of EE calculation is biased. To resolve this issue, heart rate (HR) and heart rate reserve (HRR) were used as compensatory factors respectively to correct the classical empirical formula of the accelerometer analyzer for EE in this study.

Methods

To explore the improvement of the accuracy of EE during uphill exercise and compare the correction levels between HR and HRR, oxygen uptake was used as a criterion measure (CM). Thirty healthy adult males wore an ActiGraph GT3X with the Polar HR monitor and Vmax indirect calorimeter during twelve treadmill activities (3 gradients and 4 speeds).

Results

When the slopes were increased by 0%, 3%, and 6%, the measurement accuracy of the accelerometers, calculated by intraclass correlation coefficient (ICC), decreased by 0.877, 0.755, and 0.504, respectively (p < 0.05). The HR and HRR parameters of linear regression were used to correct the classical formula. The results showed that HR had higher coefficients of determination (R2) (0.801, 0.700, and 0.642 respectively) and ICCs (0.887, 0.825, and 0.785 respectively) than did the accelerometer outputs. HRR showed the highest coefficients of determination (R2) (0.821, 0.728, and 0.656 respectively) and ICCs (0.901, 0.844, and 0.795 respectively).

Conclusions

Through adding HRR parameters, the accuracy of the classical prediction formula EE was significantly improved during walking/running on sloping ground.

The role of physical activity in the development of first cardiovascular disease event: a tree-structured survival analysis of the Danish ADDITION-PRO cohort

Background

Ambiguity exists in relation to the role of physical activity (PA) for cardiovascular disease (CVD) risk reduction. We examined the interplay between PA dimensions and more conventional CVD risk factors to assess which PA dimensions were associated with the first CVD event and whether subgroup differences exist.

Methods

A total of 1449 individuals [median age 65.8 (IQR: 61.2, 70.7) years] with low to high risk of type 2 diabetes and free from CVD from the Danish ADDITION-PRO study were included for survival analysis. PA was measured by individually calibrated heart rate and movement sensing for 7 consecutive days. The associations of different PA dimensions (PA energy expenditure, time spent in light-, moderate- and vigorous intensity PA), sedentary time and other conventional CVD risk factors with the first CVD event were examined by tree-structured survival analysis. Baseline information was linked to data on the first CVD event (ischemic heart disease, ischemic stroke, heart failure, atrial flutter/fibrillation and atherosclerotic disease) and mortality obtained from Danish registers.

Results

During a median follow-up time of 5.5 (IQR: 5.1–6.1) years, a total of 201 individuals (13.9%) developed CVD. Overall CVD incidence rate was 2.6/100 person-years. PA energy expenditure above 43 kJ/kg/day was associated with lower rates of CVD events among participants ≤ 70 years and with HbA_1c ≤ 5.7% (39 mmol/mol), systolic blood pressure ≤ 156 mmHg and albumin creatinine ratio ≤ 70 (incidence rates 0.0–0.8/100 person-years).

Conclusions

Any type of PA resulting in increased PA energy expenditure may over time be the best prevention strategy to uphold reduced risk of CVD.

Electronic supplementary material

The online version of this article (10.1186/s12933-018-0769-x) contains supplementary material, which is available to authorized users.

Prior automatic posture and activity identification improves physical activity energy expenditure prediction from hip-worn triaxial accelerometry

Accelerometry is increasingly used to quantify physical activity (PA) and related energy expenditure (EE). Linear regression models designed to derive PAEE from accelerometry-counts have shown their limits, mostly due to the lack of consideration of the nature of activities performed. Here we tested whether a model coupling an automatic activity/posture recognition (AAR) algorithm with an activity-specific count-based model, developed in 61 subjects in laboratory conditions, improved PAEE and total EE (TEE) predictions from a hip-worn triaxial-accelerometer (ActigraphGT3X+) in free-living conditions. Data from two independent subject groups of varying body mass index and age were considered: 20 subjects engaged in a 3-h urban-circuit, with activity-by-activity reference PAEE from combined heart-rate and accelerometry monitoring (Actiheart); and 56 subjects involved in a 14-day trial, with PAEE and TEE measured using the doubly-labeled water method. PAEE was estimated from accelerometry using the activity-specific model coupled to the AAR algorithm (AAR model), a simple linear model (SLM), and equations provided by the companion-software of used activity-devices (Freedson and Actiheart models). AAR-model predictions were in closer agreement with selected references than those from other count-based models, both for PAEE during the urban-circuit (RMSE = 6.19 vs 7.90 for SLM and 9.62 kJ/min for Freedson) and for EE over the 14-day trial, reaching Actiheart performances in the latter (PAEE: RMSE = 0.93 vs. 1.53 for SLM, 1.43 for Freedson, 0.91 MJ/day for Actiheart; TEE: RMSE = 1.05 vs. 1.57 for SLM, 1.70 for Freedson, 0.95 MJ/day for Actiheart). Overall, the AAR model resulted in a 43% increase of daily PAEE variance explained by accelerometry predictions.

NEW & NOTEWORTHY Although triaxial accelerometry is widely used in free-living conditions to assess the impact of physical activity energy expenditure (PAEE) on health, its precision and accuracy are often debated. Here we developed and validated an activity-specific model which, coupled with an automatic activity-recognition algorithm, improved the variance explained by the predictions from accelerometry counts by 43% of daily PAEE compared with models relying on a simple relationship between accelerometry counts and EE.

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.

Energy balance components in persons with paraplegia: daily variation and appropriate measurement duration

Background

Despite obesity being highly prevalent in persons with spinal cord injury (SCI), our current understanding of the interactions between energy balance components, which may contribute to this, is limited. The primary aim of this study is to identify the intra-individual variability of physical activity dimensions across days and suggest an appropriate monitoring time frame for these constructs in adults with SCI. The secondary aim is to examine these parameters with regard to energy intake and dietary macronutrient composition.

Methods

Participants [33 men and women with chronic (> 1 year post injury) paraplegia; age = 44 ± 9 years (mean ± S.D.] wore an Actiheart™ PA monitor and completed a weighed food diary for 7 consecutive days. Spearman-Brown Prophecy Formulae, based on Intraclass Correlations of .80 (acceptable reliability), were used to predict the number of days required to measure energy balance components. Linear mixed-effects analyses and magnitude-based inferences were performed for all energy intake, expenditure and physical activity dimensions. Adjustments were made for age, injury level, wear time, sex, day of the week and measurement order as fixed effects.

Results

To reliably measure energy expenditure components; 1 day [total energy expenditure (TEE)], 2 days [physical activity energy expenditure (PAEE), light-intensity activity, moderate-to-vigorous PA (MVPA)], 3 days [physical activity level (PAL)] and 4 days (sedentary behaviour) are necessary. Device wear time (P < 0.02), injury level (P < 0.04) and sex (P < 0.001) were covariates for energy expenditure components. Four and ≤24 days are required to reliably measure total energy intake (kcal) and diet macronutrient composition (%), respectively. Measurement order (from day 1–7) was a covariate for total energy intake (P = 0.01).

Conclusions

This is the first study to demonstrate the variability of energy intake and expenditure components in free-living persons with chronic (> 1 year) paraplegia and propose suitable measurement durations to achieve acceptable reliability in outcome measures. Device wear time and measurement order play a role in the quality of energy expenditure and intake data, respectively, and should be considered when designing and analysing studies of energy balance components in persons with SCI.

Trial registration

N/A

Applications of deuterium oxide in human health

The main aim goal of this review was to gather information about recent publications related to deuterium oxide (D₂O), and its use as a scientific tool related to human health. Searches were made in electronic databases Pubmed, Scielo, Lilacs, Medline and Cochrane. Moreover, the following patent databases were consulted: EPO (Espacenet patent search), USPTO (United States Patent and Trademark Office) and Google Patents, which cover researches worldwide related to innovations using D₂O.

An original piecewise model for computing energy expenditure from accelerometer and heart rate signals

OBJECTIVE

Activity energy expenditure (EE) plays an important role in healthcare, therefore, accurate EE measures are required. Currently available reference EE acquisition methods, such as doubly labeled water and indirect calorimetry, are complex, expensive, uncomfortable, and/or difficult to apply on real time. To overcome these drawbacks, the goal of this paper is to propose a model for computing EE in real time (minute-by-minute) from heart rate and accelerometer signals.

APPROACH

The proposed model, which consists of an original branched model, uses heart rate signals for computing EE on moderate to vigorous physical activities and a linear combination of heart rate and counts per minute for computing EE on light to moderate physical activities. Model parameters were estimated from a given data set composed of 53 subjects performing 25 different physical activities (light-, moderate- and vigorous-intensity), and validated using leave-one-subject-out. A different database (semi-controlled in-city circuit), was used in order to validate the versatility of the proposed model. Comparisons are done versus linear and nonlinear models, which are also used for computing EE from accelerometer and/or HR signals.

MAIN RESULTS

The proposed piecewise model leads to more accurate EE estimations ([Formula: see text], [Formula: see text] and [Formula: see text] J kg^-1 min^-1 and [Formula: see text], [Formula: see text], and [Formula: see text] J kg^-1 min^-1 on each validation database).

SIGNIFICANCE

This original approach, which is more conformable and less expensive than the reference methods, allows accurate EE estimations, in real time (minute-by-minute), during a large variety of physical activities. Therefore, this model may be used on applications such as computing the time that a given subject spent on light-intensity physical activities and on moderate to vigorous physical activities (binary classification accuracy of 0.8155).

Assessment of laboratory and daily energy expenditure estimates from consumer multi-sensor physical activity monitors

Wearable physical activity monitors are growing in popularity and provide the opportunity for large numbers of the public to self-monitor physical activity behaviours. The latest generation of these devices feature multiple sensors, ostensibly similar or even superior to advanced research instruments. However, little is known about the accuracy of their energy expenditure estimates. Here, we assessed their performance against criterion measurements in both controlled laboratory conditions (simulated activities of daily living and structured exercise) and over a 24 hour period in free-living conditions. Thirty men (n = 15) and women (n = 15) wore three multi-sensor consumer monitors (Microsoft Band, Apple Watch and Fitbit Charge HR), an accelerometry-only device as a comparison (Jawbone UP24) and validated research-grade multi-sensor devices (BodyMedia Core and individually calibrated Actiheart™). During discrete laboratory activities when compared against indirect calorimetry, the Apple Watch performed similarly to criterion measures. The Fitbit Charge HR was less consistent at measurement of discrete activities, but produced similar free-living estimates to the Apple Watch. Both these devices underestimated free-living energy expenditure (-394 kcal/d and -405 kcal/d, respectively; P<0.01). The multi-sensor Microsoft Band and accelerometry-only Jawbone UP24 devices underestimated most laboratory activities and substantially underestimated free-living expenditure (-1128 kcal/d and -998 kcal/d, respectively; P<0.01). None of the consumer devices were deemed equivalent to the reference method for daily energy expenditure. For all devices, there was a tendency for negative bias with greater daily energy expenditure. No consumer monitors performed as well as the research-grade devices although in some (but not all) cases, estimates were close to criterion measurements. Thus, whilst industry-led innovation has improved the accuracy of consumer monitors, these devices are not yet equivalent to the best research-grade devices or indeed equivalent to each other. We propose independent quality standards and/or accuracy ratings for consumer devices are required.

Wearable Sensors for Personal Monitoring and Estimation of Inhaled Traffic-Related Air Pollution: Evaluation of Methods

Physical activity and ventilation rates have an effect on an individual's dose and may be important to consider in exposure-response relationships; however, these factors are often ignored in environmental epidemiology studies. The aim of this study was to evaluate methods of estimating the inhaled dose of air pollution and understand variability in the absence of a true gold standard metric. Five types of methods were identified: (1) methods using (physical) activity types, (2) methods based on energy expenditure, METs (metabolic equivalents of task), and oxygen consumption, (3) methods based on heart rate or (4) breathing rate, and (5) methods that combine heart and breathing rate. Methods were compared using a real-life data set of 122 adults who wore devices to track movement, black carbon air pollution, and physiological health markers for 3 weeks in three European cities. Different methods for estimating minute ventilation performed well in relative terms with high correlations among different methods, but in absolute terms, ignoring increased ventilation during day-to-day activities could lead to an underestimation of the daily dose by a factor of 0.08-1.78. There is no single best method, and a multitude of methods are currently being used to approximate the dose. The choice of a suitable method for determining the dose in future studies will depend on both the size and the objectives of the study.

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

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

Energy Expenditure in People with Diabetes Mellitus: A Review

Physical activity (PA) is an important non-therapeutic tool in primary prevention and treatment of diabetes mellitus (DM). To improve activity-based health management, patients need to quantify activity-related energy expenditure and the other components of total daily energy expenditure. This review explores differences between the components of total energy expenditure in patients with DM and healthy people and presents various tools for assessing the energy expenditure in subjects with DM. From this review, it appears that patients with uncontrolled DM have a higher basal energy expenditure (BEE) than healthy people which must be considered in the establishment of new BEE estimate equations. Moreover, studies showed a lower activity energy expenditure in patients with DM than in healthy ones. This difference may be partially explained by patient with DMs poor compliance with exercise recommendations and their greater participation in lower intensity activities. These specificities of PA need to be taken into account in the development of adapted tools to assess activity energy expenditure and daily energy expenditure in people with DM. Few estimation tools are tested in subjects with DM and this results in a lack of accuracy especially for their particular patterns of activity. Thus, future studies should examine sensors coupling different technologies or method that is specifically designed to accurately assess energy expenditure in patients with diabetes in daily life.

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

BACKGROUND

Wrist-worn accelerometers are emerging as the most common instrument for measuring physical activity in large-scale epidemiological studies, though little is known about the relationship between wrist acceleration and physical activity energy expenditure (PAEE).

METHODS

1695 UK adults wore two devices simultaneously for six days; a combined sensor and a wrist accelerometer. The combined sensor measured heart rate and trunk acceleration, which was combined with a treadmill test to yield a signal of individually-calibrated PAEE. Multi-level regression models were used to characterise the relationship between the two time-series, and their estimations were evaluated in an independent holdout sample. Finally, the relationship between PAEE and BMI was described separately for each source of PAEE estimate (wrist acceleration models and combined-sensing).

RESULTS

Wrist acceleration explained 44-47% between-individual variance in PAEE, with RMSE between 34-39 J•min-1•kg-1. Estimations agreed well with PAEE in cross-validation (mean bias [95% limits of agreement]: 0.07 [-70.6:70.7]) but overestimated in women by 3% and underestimated in men by 4%. Estimation error was inversely related to age (-2.3 J•min-1•kg-1 per 10y) and BMI (-0.3 J•min-1•kg-1 per kg/m2). Associations with BMI were similar for all PAEE estimates (approximately -0.08 kg/m2 per J•min-1•kg-1).

CONCLUSIONS

A strong relationship exists between wrist acceleration and PAEE in free-living adults, such that irrespective of the objective method of PAEE assessment, a strong inverse association between PAEE and BMI was observed.

Estimation of Energy Expenditure Using a Patch-Type Sensor Module with an Incremental Radial Basis Function Neural Network

Conventionally, indirect calorimetry has been used to estimate oxygen consumption in an effort to accurately measure human body energy expenditure. However, calorimetry requires the subject to wear a mask that is neither convenient nor comfortable. The purpose of our study is to develop a patch-type sensor module with an embedded incremental radial basis function neural network (RBFNN) for estimating the energy expenditure. The sensor module contains one ECG electrode and a three-axis accelerometer, and can perform real-time heart rate (HR) and movement index (MI) monitoring. The embedded incremental network includes linear regression (LR) and RBFNN based on context-based fuzzy c-means (CFCM) clustering. This incremental network is constructed by building a collection of information granules through CFCM clustering that is guided by the distribution of error of the linear part of the LR model.

Towards human exploration of space: The THESEUS review series on nutrition and metabolism research priorities

Nutrition has multiple roles during space flight from providing sufficient nutrients to meet the metabolic needs of the body and to maintain good health, to the beneficial psychosocial aspects related to the meals. Nutrition is central to the functioning of the body; poor nutrition compromises all the physiological systems. Nutrition is therefore likely to have a key role in counteracting the negative effects of space flight (e.g., radiation, immune deficits, oxidative stress, and bone and muscle loss). As missions increase in duration, any dietary/nutritional deficiencies will become progressively more detrimental. Moreover, it has been recognized that the human diet contains, in addition to essential macronutrients, a complex array of naturally occurring bioactive micronutrients that may confer significant long-term health benefits. It is therefore critical that astronauts be adequately nourished during missions. Problems of nutritional origin are often treatable by simply providing the appropriate nutrients and adequate recommendations. This review highlights six key issues that have been identified as space research priorities in nutrition field: in-flight energy balance; altered feeding behavior; development of metabolic stress; micronutrient deficiency; alteration of gut microflora; and altered fluid and electrolytes balance. For each of these topics, relevance for space exploration, knowledge gaps and proposed investigations are described. Finally, the nutritional questions related to bioastronautics research are very relevant to multiple ground-based-related health issues. The potential spin-offs are both interesting scientifically and potentially of great clinical importance.

Assessment of sedentary behaviors and transport-related activities by questionnaire: a validation study

Background

Comprehensive assessment of sedentary behavior (SB) and physical activity (PA), including transport-related activities (TRA), is required to design innovative PA promotion strategies. There are few validated instruments that simultaneously assess the different components of human movement according to their context of practice (e.g. work, transport, leisure). We examined test-retest reliability and validity of the Sedentary, Transportation and Activity Questionnaire (STAQ), a newly developed questionnaire dedicated to assessing context-specific SB, TRA and PA.

Methods

Ninety six subjects (51 women) kept a contextualized activity-logbook and wore a hip accelerometer (Actigraph GT3X + ^TM) for a 7-day or 14-day period, at the end of which they completed the STAQ. Activity-energy expenditure was measured in a subgroup of 45 subjects using the double labeled water (DLW) method. Test-retest reliability was assessed using intra-class-coefficients (ICC) in a subgroup of 32 subjects who filled the questionnaire twice one month apart. Accelerometry was annotated using the logbook to obtain total and context-specific objective estimates of SB. Spearman correlations, Bland-Altman plots and ICC were used to analyze validity with logbook, accelerometry and DLW data validity criteria.

Results

Test-retest reliability was fair for total sitting time (ICC = 0.52), good to excellent for work sitting time (ICC = 0.71), transport-related walking (ICC = 0.61) and car use (ICC = 0.67), and leisure screen-related SB (ICC = 0.64-0.79), but poor for total sitting time during leisure and transport-related contexts. For validity, compared to accelerometry, significant correlations were found for STAQ estimates of total (r = 0.54) and context-specific sitting times with stronger correlations for work sitting time (r = 0.88), and screen times (TV/DVD viewing: r = 0.46; other screens: r = 0.42) than for transport (r = 0.35) or leisure-related sitting-times (r = 0.19). Compared to contextualized logbook, STAQ estimates of TRA was higher for car (r = 0.65) than for active transport (r = 0.41). The questionnaire generally overestimated work- and leisure-related SB and sitting times, while it underestimated total and transport-related sitting times.

Conclusions

The STAQ showed acceptable reliability and a good ranking validity for assessment of context-specific SB and TRA. This instrument appears as a useful tool to study SB, TRA and PA in context in adults.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-016-3412-3) contains supplementary material, which is available to authorized users.

Metabolic Equivalent in Adolescents, Active Adults and Pregnant Women

“Metabolic Equivalent” (MET) represents a standard amount of oxygen consumed by the body under resting conditions, and is defined as 3.5 mL O₂/kg × min or ~1 kcal/kg × h. It is used to express the energy cost of physical activity in multiples of MET. However, universal application of the 1-MET standard was questioned in previous studies, because it does not apply well to all individuals. Height, weight and resting metabolic rate (RMR, measured by indirect calorimetry) were measured in adolescent males (n = 50) and females (n = 50), women during pregnancy (gestation week 35–41, n = 46), women 24–53 weeks postpartum (n = 27), and active men (n = 30), and were compared to values predicted by the 1-MET standard. The RMR of adolescent males (1.28 kcal/kg × h) was significantly higher than that of adolescent females (1.11 kcal/kg × h), with or without the effects of puberty stage and physical activity levels. The RMR of the pregnant and post-pregnant subjects were not significantly different. The RMR of the active normal weight (0.92 kcal/kg × h) and overweight (0.89 kcal/kg × h) adult males were significantly lower than the 1-MET value. It follows that the 1-MET standard is inadequate for use not only in adult men and women, but also in adolescents and physically active men. It is therefore recommended that practitioners estimate RMR with equations taking into account individual characteristics, such as sex, age and Body Mass Index, and not rely on the 1-MET standard.

Impact of sit-stand desks at work on energy expenditure and sedentary time: protocol for a feasibility study

BACKGROUND

Prolonged sitting, an independent risk factor for disease development and premature mortality, is increasing in prevalence in high- and middle-income countries, with no signs of abating. Adults in such countries spend the largest proportion of their day in sedentary behaviour, most of which is accumulated at work. One promising method for reducing workplace sitting is the use of sit-stand desks. However, key uncertainties remain about this intervention, related to the quality of existing studies and a lack of focus on key outcomes, including energy expenditure. We are planning a randomised controlled trial to assess the impact of sit-stand desks at work on energy expenditure and sitting time in the short and longer term. To reduce the uncertainties related to the design of this trial, we propose a preliminary study to assess the feasibility and acceptability of the recruitment, allocation, measurement, retention and intervention procedures.

METHODS

Five hundred office-based employees from two companies in Cambridge, UK, will complete a survey to assess their interest in participating in a trial on the use of sit-stand desks at work. The workspaces of 100 of those interested in participating will be assessed for sit-stand desk installation suitability, and 20 participants will be randomised to either the use of sit-stand desks at work for 3 months or a waiting list control group. Energy expenditure and sitting time, measured via Actiheart and activPAL monitors, respectively, as well as cardio-metabolic and anthropometric outcomes and other outcomes relating to health and work performance, will be assessed in 10 randomly selected participants. All participants will also be interviewed about their experience of using the desks and participating in the study.

DISCUSSION

The findings are expected to inform the design of a trial assessing the impact of sit-stand desks at work on short and longer term workplace sitting, taking into account their impact on energy expenditure and the extent to which their use has compensation effects outside the workplace. The findings from such a trial are expected to inform discussions regarding the potential of sit-stand desks at work to alleviate the harm to cardio-metabolic health arising from prolonged sitting.

TRIAL REGISTRATION

ISRCTN44827407.

Alterations in energy balance from an exercise intervention with ad libitum food intake

Better understanding is needed regarding the effects of exercise alone, without any imposed dietary regimens, as a single tool for body-weight regulation. Thus, we evaluated the effects of an 8-week increase in activity energy expenditure (AEE) on ad libitum energy intake (EI), body mass and composition in healthy participants with baseline physical activity levels (PAL) in line with international recommendations. Forty-six male adults (BMI = 19·7–29·3 kg/m²) participated in an intervention group, and ten (BMI = 21·0–28·4 kg/m²) in a control group. Anthropometric measures, cardiorespiratory fitness, EI, AEE and exercise intensity were recorded at baseline and during the 1st, 5th and 8th intervention weeks, and movement was recorded throughout. Body composition was measured at the beginning and at the end of the study, and resting energy expenditure was measured after the study. The intervention group increased PAL from 1·74 (se 0·03) to 1·93 (se 0·03) (P < 0·0001) and cardiorespiratory fitness from 41·4 (se 0·9) to 45·7 (se 1·1) ml O₂/kg per min (P = 0·001) while decreasing body mass (−1·36 (se 0·2) kg; P = 0·001) through adipose tissue mass loss (ATM) (−1·61 (se 0·2) kg; P = 0·0001) compared with baseline. The control group did not show any significant changes in activity, body mass or ATM. EI was unchanged in both groups. The results indicate that in normal-weight and overweight men, increasing PAL from 1·7 to 1·9 while keeping EI ad libitum over an 8-week period produces a prolonged negative energy balance. Replication using a longer period (and/or more intense increase in PAL) is needed to investigate if and at what body composition the increase in AEE is met by an equivalent increase in EI.

The causal role of breakfast in energy balance and health: a randomized controlled trial in obese adults

BACKGROUND

The causal nature of associations between breakfast and health remain unclear in obese individuals.

OBJECTIVE

We sought to conduct a randomized controlled trial to examine causal links between breakfast habits and components of energy balance in free-living obese humans.

DESIGN

The Bath Breakfast Project is a randomized controlled trial with repeated measures at baseline and follow-up among a cohort in South West England aged 21-60 y with dual-energy X-ray absorptiometry-derived fat mass indexes of ≥13 kg/m(2) for women (n = 15) and ≥9 kg/m(2) for men (n = 8). Components of energy balance (resting metabolic rate, physical activity thermogenesis, diet-induced thermogenesis, and energy intake) were measured under free-living conditions with random allocation to daily breakfast (≥700 kcal before 1100) or extended fasting (0 kcal until 1200) for 6 wk, with baseline and follow-up measures of health markers (e.g., hematology/adipose biopsies).

RESULTS

Breakfast resulted in greater physical activity thermogenesis during the morning than when fasting during that period (difference: 188 kcal/d; 95% CI: 40, 335) but without any consistent effect on 24-h physical activity thermogenesis (difference: 272 kcal/d; 95% CI: -254, 798). Energy intake was not significantly greater with breakfast than fasting (difference: 338 kcal/d; 95% CI: -313, 988). Body mass increased across both groups over time but with no treatment effects on body composition or any change in resting metabolic rate (stable within 8 kcal/d). Metabolic/cardiovascular health also did not respond to treatments, except for a reduced insulinemic response to an oral-glucose-tolerance test over time with daily breakfast relative to an increase with daily fasting (P = 0.05).

CONCLUSIONS

In obese adults, daily breakfast leads to greater physical activity during the morning, whereas morning fasting results in partial dietary compensation (i.e., greater energy intake) later in the day. There were no differences between groups in weight change and most health outcomes, but insulin sensitivity increased with breakfast relative to fasting. This trial was registered at www.isrctn.org as ISRCTN31521726.