Deciphering the constrained total energy expenditure model in humans by associating accelerometer-measured physical activity from wrist and hip

Validity of predictive equations for total energy expenditure against doubly labeled water

Variations in physical activity energy expenditure can make accurate prediction of total energy expenditure (TEE) challenging. The purpose of the present study was to determine the accuracy of available equations to predict TEE in individuals varying in physical activity (PA) levels. TEE was measured by DLW in 56 adults varying in PA levels which were monitored by accelerometry. Ten different models were used to predict TEE and their accuracy and precision were evaluated, considering the effect of sex and PA. The models generally underestimated the TEE in this population. An equation published by Plucker was the most accurate in predicting the TEE in our entire sample. The Pontzer and Vinken models were the most accurate for those with lower PA levels. Despite the levels of accuracy of some equations, there were sizable errors (low precision) at an individual level. Future studies are needed to develop and validate these equations.

Large-scale assessment of physical activity in a population using high-resolution hip-worn accelerometry: the German National Cohort (NAKO)

Large population-based cohort studies utilizing device-based measures of physical activity are crucial to close important research gaps regarding the potential protective effects of physical activity on chronic diseases. The present study details the quality control processes and the derivation of physical activity metrics from 100 Hz accelerometer data collected in the German National Cohort (NAKO). During the 2014 to 2019 baseline assessment, a subsample of NAKO participants wore a triaxial ActiGraph accelerometer on their right hip for seven consecutive days. Auto-calibration, signal feature calculations including Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD), identification of non-wear time, and imputation, were conducted using the R package GGIR version 2.10-3. A total of 73,334 participants contributed data for accelerometry analysis, of whom 63,236 provided valid data. The average ENMO was 11.7 ± 3.7 mg (milli gravitational acceleration) and the average MAD was 19.9 ± 6.1 mg. Notably, acceleration summary metrics were higher in men than women and diminished with increasing age. Work generated in the present study will facilitate harmonized analysis, reproducibility, and utilization of NAKO accelerometry data. The NAKO accelerometry dataset represents a valuable asset for physical activity research and will be accessible through a specified application process.

Time reallocation of physical behaviors induced by endurance exercise in physically active individuals

ABSTRACT

Increasing moderate-vigorous physical activity (MVPA) through exercise requires reallocating time from other physical behavior(s). We aimed to determine the reallocations induced by endurance exercise in physically active individuals. We also searched for behavioral compensatory responses, and explored the effect of exercise on daily energy expenditure. Fourteen participants (8 women; median age 37.8 [IQR 29.9–48.5] yr) exercised on Monday, Wednesday, and Friday mornings (cycling MVPA, 65 min/session; "exercise days"), and avoided exercising on Tuesday and Thursday ("rest days"). Time spent on sleep, sedentary behavior, light-intensity physical activity, and MVPA was determined each day by accelerometers and logs. An energy expenditure index was computed considering minutes spent on each behavior and fixed metabolic equivalents. We found that all participants had lower sleep and higher total (including exercise) MVPA on exercise days compared to rest days. Thus, on exercise vs. rest days, sleep was lower (490 [453–553] vs. 553 [497–599] min/day, respectively, P<0.001), and total MVPA was higher (86 [80–101] vs. 23 [15–45] min/day, respectively; P<0.001). No differences in other physical behaviors were detected. Notably, exercise not only induced reallocations (i.e. less time in other behaviors) but also behavioral compensatory responses in some participants (e.g. increased sedentary behavior). This rearrangement of physical behaviors manifested in exercise-induced increases in energy expenditure from 96 to 232 MET×min/day. In conclusion, active individuals reallocated time from sleep to accommodate morning exercise. Yet exercise induced variable rearrangements of behaviors, with some individuals manifesting compensatory responses. Understanding individual rearrangements may help improve exercise interventions.

"suMus," a novel digital system for arm movement metrics and muscle energy expenditure

Objective: In the field of non-treatable muscular dystrophies, promising new gene and cell therapies are being developed and are entering clinical trials. Objective assessment of therapeutic effects on motor function is mandatory for economical and ethical reasons. Main shortcomings of existing measurements are discontinuous data collection in artificial settings as well as a major focus on walking, neglecting the importance of hand and arm movements for patients' independence. We aimed to create a digital tool to measure muscle function with an emphasis on upper limb motility. Methods: suMus provides a custom-made App running on smartwatches. Movement data are sent to the backend of a suMus web-based platform, from which they can be extracted as CSV data. Fifty patients with neuromuscular diseases assessed the pool of suMus activities in a first orientation phase. suMus performance was hence validated in four upper extremity exercises based on the feedback of the orientation phase. We monitored the arm metrics in a cohort of healthy volunteers using the suMus application, while completing each exercise at low frequency in a metabolic chamber. Collected movement data encompassed average acceleration, rotation rate as well as activity counts. Spearman rank tests correlated movement data with energy expenditure from the metabolic chamber. Results: Our novel application "suMus," sum of muscle activity, collects muscle movement data plus Patient-Related-Outcome-Measures, sends real-time feedback to patients and caregivers and provides, while ensuring data protection, a long-term follow-up of disease course. The application was well received from the patients during the orientation phase. In our pilot study, energy expenditure did not differ between overnight fasted and non-fasted participants. Acceleration ranged from 1.7 ± 0.7 to 3.2 ± 0.5 m/sec² with rotation rates between 0.9 ± 0.5 and 2.0 ± 3.4 rad/sec. Acceleration and rotation rate as well as derived activity counts correlated with energy expenditure values measured in the metabolic chamber for one exercise (r = 0.58, p < 0.03). Conclusion: In the analysis of slow frequency movements of upper extremities, the integration of the suMus application with smartwatch sensors characterized motion parameters, thus supporting a use in clinical trial outcome measures. Alternative methodologies need to complement indirect calorimetry in validating accelerometer-derived energy expenditure data.

Methods recently used for the assessment of physical activity in children and adolescents

PURPOSE OF REVIEW

The aim of this review is to summarize recent evidences and advances on the implementation and the use of new tools to assessing physical activity (PA) in children.

RECENT FINDINGS

Technological advances provide, increasingly, new objective methods for the evaluation of PA in children. In addition to accelerometry, there are other objective methods for assessing PA in children such as new wearable monitoring and activity bracelets, smartphone and recording software applications, Global Positioning System or Inertial Sensors Devices.

SUMMARY

Doubly labeled water and calorimetry are reference methods to assessing PA but with limitations of use. Accelerometry is an accurate method for measuring sedentary behavior and PA levels in children. In fact, it is a real alternative reference method for the validation of methods and tools of assessing PA. However, there is still no consensus about the most appropriate approach to analyze the duration and intensity of PA in children. Therefore, the implementation of other alternative objective methods, as well as complementation with PA questionnaires, can provide a more precise evaluation of different patterns and behaviors related with sedentarism and PA.

The relationship between total energy expenditure and physical activity level in women living in an impoverished Brazilian urban area

OBJECTIVE

This study aimed to describe how strong is the relationship between TEE and PAL in women living in an impoverished Brazilian urban area.

METHODS

Anthropometric, hormonal (insulin, TSH, FT₄ , and FT₃ ), body composition (deuterium), TEE (doubly labeled water) and PAL (metabolic equivalent task [MET]-7-day triaxial accelerometer, ActivPAL®) data were collected from 55 women (mean age: 31y, mean BMI: 27.4 kg/m² ). Adjusted-TEE models were calculated incorporating the residuals of anthropometric, hormonal, and body composition variables in the TEE, to assess the relation between MET and adjusted-TEE, through three different analyzes: linear regression, nonlinear regression and change-point regression.

RESULTS

Most participants (89.1%, n = 49) were classified as low-active. There was no association between crude TEE and MET.h/d (R²  = 0.05; p = 0.09). There was a positive, although weak, linear relationship between adjusted-TEE and MET.h/d (β = 2705.26 kcal; 95% CI: 436.25; 4974.27; adj-R²  = 0.08). A change point of this relationship was identified in the MET.h/d value of 1.53 (SD = 0.02; adj-R² = 0.13). The association between adjusted-TEE and MET.h/d before the change point (β = 5101.46 kcal; 95%CI: 978.84; 9224.08; adj-R²  = 0.11) was stronger than the association in the full linear model, nevertheless, it was still weak. This association stop existing when it is assessed in individuals after the change point (β = -6609.92 kcal; 95%CI: -16773.24; 3553.40; adj-R²  = 0.08).

CONCLUSIONS

These results suggest that the relationship between TEE and PAL is weak and it is not completely linear in women living in an impoverished Brazilian urban area.

The Mystery of Energy Compensation

AbstractThe received wisdom on how activity affects energy expenditure is that the more activity is undertaken, the more calories will have been burned by the end of the day. Yet traditional hunter-gatherers, who lead physically hard lives, burn no more calories each day than Western populations living in labor-saving environments. Indeed, there is now a wealth of data, both for humans and other animals, demonstrating that long-term lifestyle changes involving increases in exercise or other physical activities do not result in commensurate increases in daily energy expenditure (DEE). This is because humans and other animals exhibit a degree of energy compensation at the organismal level, ameliorating some of the increases in DEE that would occur from the increased activity by decreasing the energy expended on other biological processes. And energy compensation can be sizable, reaching many hundreds of calories in humans. But the processes that are downregulated in the long-term to achieve energy compensation are far from clear, particularly in humans-we do not know how energy compensation is achieved. My review here of the literature on relevant exercise intervention studies, for both humans and other species, indicates conflict regarding the role, if any, of basal metabolic rate (BMR) or low-level activity such as fidgeting play, particularly once changes in body composition are factored out. In situations where BMR and low-level activity are not major components of energy compensation, what then drives it? I discuss how changes in mitochondrial efficiency and changes in circadian fluctuations in BMR may contribute to our understanding of energy management. Currently unexplored, these mechanisms and others may provide important insights into the mystery of how energy compensation is achieved.