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Competing Interests: Competing interests statement:The authors declare no competing interest.

We tested whether spontaneous physical activity (SPA) from accelerometers could be used in a whole room calorimeter to estimate thermic effect of food (TEF). Eleven healthy participants ( n = 7 females; age: 27 ± 4 yr; body mass index: 22.8 ± 2.6 kg/m 2 ) completed two 23-h visits in randomized order: one "fed" with meals provided and one "fasted" with no food. SPA was measured by ActivPAL and Actigraph accelerometers. Criterion TEF was calculated as the difference in total daily energy expenditure (TDEE) between fed and fasted visits and compared with three methods of estimating TEF: 1 ) SPA-adjusted TEF (adjTEF)-difference in TDEE without SPA between visits, 2 ) Wakeful TEF-difference in energy expenditure obtained from linear regression and basal metabolic rate during waking hours, 3 ) 24-h TEF-increase in TDEE above SPA and sleeping metabolic rate. Criterion TEF was 9.4 ± 4.5% of TDEE. AdjTEF (difference in estimated vs. criterion TEF: activPAL: -0.3 ± 3.3%; Actigraph: -1.8 ± 8.0%) and wakeful TEF (activPAL: -0.9 ± 6.1%; Actigraph: -2.8 ± 7.6%) derived from both accelerometers did not differ from criterion TEF (all P > 0.05). ActivPAL-derived 24-h TEF overestimated TEF (6.8 ± 5.4%, P = 0.002), whereas Actigraph-derived 24-h TEF was not significantly different (4.3 ± 9.4%, P = 0.156). TEF estimations using activPAL tended to show better individual-level agreement (i.e., smaller coefficients of variation). Both accelerometers can be used to estimate TEF in a whole room calorimeter; wakeful TEF using activPAL is the most viable option given strong group-level accuracy and reasonable individual agreement. NEW & NOTEWORTHY Two research-grade accelerometers can effectively estimate spontaneous physical activity and improve the estimation of thermic effect of food (TEF) in whole room calorimeters. The activPAL demonstrates strong group-level accuracy and reasonable individual-level agreement in estimating wakeful TEF, whereas a hip-worn Actigraph is an acceptable approach for estimating 24-h TEF. These results highlight the promising potential of accelerometers in advancing energy balance research by improving the assessment of TEF within whole room calorimeters.

Metabolic syndrome poses a significant health challenge worldwide, prompting the need for comprehensive strategies integrating physical activity monitoring and energy expenditure. Wearable sensor devices have been used both for energy intake and energy expenditure (EE) estimation. Traditionally, sensors are attached to the hip or wrist. The primary aim of this research is to investigate the use of an eyeglass-mounted wearable energy intake sensor (Automatic Ingestion Monitor v2, AIM-2) for simultaneous recognition of physical activity (PAR) and estimation of steady-state EE as compared to a traditional hip-worn device. Study data were collected from six participants performing six structured activities, with the reference EE measured using indirect calorimetry (COSMED K5) and reported as metabolic equivalents of tasks (METs). Next, a novel deep convolutional neural network-based multitasking model (Multitasking-CNN) was developed for PAR and EE estimation. The Multitasking-CNN was trained with a two-step progressive training approach for higher accuracy, where in the first step the model for PAR was trained, and in the second step the model was fine-tuned for EE estimation. Finally, the performance of Multitasking-CNN on AIM-2 attached to eyeglasses was compared to the ActiGraph GT9X (AG) attached to the right hip. On the AIM-2 data, Multitasking-CNN achieved a maximum of 95% testing accuracy of PAR, a minimum of 0.59 METs mean square error (MSE), and 11% mean absolute percentage error (MAPE) in EE estimation. Conversely, on AG data, the Multitasking-CNN model achieved a maximum of 82% testing accuracy in PAR, a minimum of 0.73 METs MSE, and 13% MAPE in EE estimation. These results suggest the feasibility of using an eyeglass-mounted sensor for both PAR and EE estimation.

Conventionally, women are perceived to feel colder than men, but controlled comparisons are sparse. We measured the response of healthy, lean, young women and men to a range of ambient temperatures typical of the daily environment (17 to 31 °C). The Scholander model of thermoregulation defines the lower critical temperature as threshold of the thermoneutral zone, below which additional heat production is required to defend core body temperature. This parameter can be used to characterize the thermoregulatory phenotypes of endotherms on a spectrum from "arctic" to "tropical." We found that women had a cooler lower critical temperature (mean ± SD: 21.9 ± 1.3 °C vs. 22.9 ± 1.2 °C, P = 0.047), resembling an "arctic" shift compared to men. The more arctic profile of women was predominantly driven by higher insulation associated with more body fat compared to men, countering the lower basal metabolic rate associated with their smaller body size, which typically favors a "tropical" shift. We did not detect sex-based differences in secondary measures of thermoregulation including brown adipose tissue glucose uptake, muscle electrical activity, skin temperatures, cold-induced thermogenesis, or self-reported thermal comfort. In conclusion, the principal contributors to individual differences in human thermoregulation are physical attributes, including body size and composition, which may be partly mediated by sex., Competing Interests: Competing interests statement:The authors declare no competing interest.

Post-infectious myalgic encephalomyelitis/chronic fatigue syndrome (PI-ME/CFS) is a disabling disorder, yet the clinical phenotype is poorly defined, the pathophysiology is unknown, and no disease-modifying treatments are available. We used rigorous criteria to recruit PI-ME/CFS participants with matched controls to conduct deep phenotyping. Among the many physical and cognitive complaints, one defining feature of PI-ME/CFS was an alteration of effort preference, rather than physical or central fatigue, due to dysfunction of integrative brain regions potentially associated with central catechol pathway dysregulation, with consequences on autonomic functioning and physical conditioning. Immune profiling suggested chronic antigenic stimulation with increase in naïve and decrease in switched memory B-cells. Alterations in gene expression profiles of peripheral blood mononuclear cells and metabolic pathways were consistent with cellular phenotypic studies and demonstrated differences according to sex. Together these clinical abnormalities and biomarker differences provide unique insight into the underlying pathophysiology of PI-ME/CFS, which may guide future intervention., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Purpose: Accelerometers are used to objectively measure physical behaviors in free-living environments, typically for seven consecutive days or more. We examined whether participants experience "wear fatigue," a decline in wear time day over day, during typical assessment period acquired in a nationally representative sample of 6- to 80-yr-olds in the United States., Methods: Participants were instructed to wear an ActiGraph GT3X+ on their nondominant wrist continuously for seven consecutive days. Participants with seven complete days of recorded data, regardless of wear status, were included in the analyses ( N = 13,649). Wear was scored with the sleep, wake, and nonwear algorithm., Results: Participants averaged 1248 ± 3.6 min·d -1 (mean ± SE) of wear over the assessment, but wear time linearly decreased from day 1 (1295 ± 3.2 min) to day 7 (1170 ± 5.3 min), resulting in a wear fatigue of -18.1 ± 0.7 min·d -1 ( β ± SE). Wear fatigue did not differ by sex but varied by age-group-highest in adolescents (-26.8 ± 2.4 min·d -1 ) and lowest in older adults (-9.3 ± 0.9 min·d -1 ). Wear was lower in evening (1800-2359 h) and early morning (0000-0559 h) compared with the middle of the day and on weekend days compared with weekdays. We verified similar wear fatigue (-23.5 ± 0.7 min·d -1 ) in a separate sample ( N = 14,631) with hip-worn devices and different wear scoring. Applying minimum wear criteria of ≥10 h·d -1 for ≥4 d reduced wear fatigue to -5.3 and -18.7 min·d -1 for the wrist and hip, respectively., Conclusions: Patterns of wear suggest noncompliance may disproportionately affect estimates of sleep and sedentary behavior, particularly for adolescents. Further study is needed to determine the effect of wear fatigue on longer assessments., (Copyright © 2023 by the American College of Sports Medicine.)

Introduction: A key component to analyzing wearable sensor data is identifying periods of non-wear. Traditionally, strings of consecutive zero counts (e.g. >60-minutes) are identified indicating periods of non-movement. The non-movement window length is then evaluated as wear or non-wear. Given that non-movement is not equivalent to non-wear, additional criteria should be evaluated to objectively identify periods of non-wear. Identifying non-wear is especially challenging in infants due to their sporadic movement, sleep frequency, and proportion of caregiver-generated movement., Purpose: To use hip- and ankle-worn ActiGraph wGT3X-BT (wGT3X-BT) data to identify non-wear in infants., Methods: Fifteen infant participants [mean±SD; age, 8.7±1.7 weeks (range 5.4-11.3 weeks); 5.1±0.8 kg; 56.2±2.1 cm; n = 8 females] wore a wGT3X-BT on the hip and ankle. Criterion data were collected during two, 2-hour directly observed periods in the laboratory. Using raw 30 Hz acceleration data, a vector magnitude and the inclination angle of each individual axis were calculated before being averaged into 1-minute windows. Three decision tree models were developed using data from 1) hip only, 2) ankle only, and 3) hip and ankle combined., Results: The hip model classified 86.6% of all minutes (wear and non-wear) correctly (F1 = 75.5%) compared to the ankle model which classified 90.6% of all minutes correctly (F1 = 83.0%). The combined site model performed similarly to the ankle model and correctly classified 90.0% of all minutes (F1 = 80.8%)., Conclusion: The similar performance between the ankle only model and the combined site model likely indicates that the features from the ankle device are more important for identifying non-wear in infants. Overall, this approach provides an advancement in the identification of device wear status using wearable sensor data in infants., Competing Interests: The authors have declared that no competing interests exist.

Bout detection algorithms are used to segment data from wearable sensors, but it is challenging to assess segmentation correctness., Purpose: To present and demonstrate the Transition Pairing Method (TPM), a new method for evaluating the performance of bout detection algorithms., Methods: The TPM compares predicted transitions to a criterion measure in terms of number and timing. A true positive is defined as a predicted transition that corresponds with one criterion transition in a mutually exclusive pair. The pairs are established using an extended Gale-Shapley algorithm, and the user specifies a maximum allowable within-pair time lag, above which pairs cannot be formed. Unpaired predictions and criteria are false positives and false negatives, respectively. The demonstration used raw acceleration data from 88 youth who wore ActiGraph GT9X monitors (right hip and non-dominant wrist) during simulated free-living. Youth Sojourn bout detection algorithms were applied (one for each attachment site), and the TPM was used to compare predicted bout transitions to the criterion measure (direct observation). Performance metrics were calculated for each participant, and hip-versus-wrist means were compared using paired T-tests (α = 0.05)., Results: When the maximum allowable lag was 1-s, both algorithms had recall <20% (2.4% difference from one another, p<0.01) and precision <10% (1.4% difference from one another, p<0.001). That is, >80% of criterion transitions were undetected, and >90% of predicted transitions were false positives., Conclusion: The TPM improves on conventional analyses by providing specific information about bout detection in a standardized way that applies to any bout detection algorithm., Competing Interests: Conflict of Interest The authors declare no conflicts of interest.

Youth metabolic equivalents (METy) are sometimes operationally defined as multiples of predicted basal metabolic rate (METyBMR) and other times as multiples of measured resting metabolic rate (METyRMR)., Purpose: This study aimed to examine the comparability of METyBMR and METyRMR., Methods: Indirect calorimetry data (Cosmed K4b) were analyzed from two studies, with a total sample of 245 youth (125 male participants, 6-18 yr old, 37.4% overweight or obese). The Schofield equations were used to predict BMR, and K4b data from 30 min of supine rest were used to assess RMR. Participants performed structured physical activities (PA) of various intensities, and steady-state oxygen consumption was divided by predicted BMR and measured RMR to calculate METyBMR and METyRMR, respectively. Two-way (activity-METy calculation) analysis of variance was used to compare METyBMR and METyRMR (α = 0.05), with Bonferroni-corrected post hoc tests. Intensity classifications were also compared after encoding METyBMR and METyRMR as sedentary behavior (≤1.50 METy), light PA (1.51-2.99 METy), moderate PA (3.00-5.99 METy), or vigorous PA (≥6.00 METy)., Results: There was a significant interaction (F(30) = 3.6, P < 0.001), and METyBMR was significantly higher than METyRMR for 28 of 31 activities (P < 0.04), by 15.6% (watching television) to 23.1% (basketball). Intensity classifications were the same for both METy calculations in 69.0% of cases., Conclusions: METyBMR and METyRMR differ considerably. Greater consensus is needed regarding how metabolic equivalents should be operationally defined in youth, and in the meantime, careful distinction is necessary between METyBMR and METyRMR.

Public health surveillance is a vital component in the assessment of health-related behaviors such as physical activity (PA). With multiple active national health surveys in the United States, questions arise about how data are collected, what each data source contributes to the overall knowledge base about PA and health outcomes, and how to interpret PA data from different data sources to gain an understanding about PA at the population level. This article highlights specifically the challenges and opportunities with using wearable devices in population-level PA assessment. A major challenge faced by PA assessment researchers is that of which assessment methods and evaluation tools to use and under what circumstances to use them. This article discusses issues related to (1) what device to use, (2) how to collect data, (3) how to process data, (4) how to analyze the data, and (5) how to report the procedures used. These decisions shape not only the data collection process including collection time and cost but also directly impact data analysis and subsequently the outcomes of interest. This article discusses the implications of using different assessment methods and evaluation tools and how the use of sensor-based tools may impact the future of PA assessment at the population level. There are a number of opportunities emerging for population-level assessment of PA due in part to the technological advances occurring with wearable technology. These opportunities may afford surveillance systems new data streams to bolster what is currently being collected to provide more robust estimates of PA and other health behaviors. The article concludes with some discussion about how the landscape of population-level PA assessment is changing, thanks to increasing opportunities to collect wearable device data. With new data streams becoming available through advanced wearable devices containing multiple sensor types and the opportunity for corporate partnerships, the way PA is being assessed could change considerably in the near future. While acknowledging the limitations of wearable technology, it is an exciting time for PA assessment, given the possibilities on the horizon., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The purpose of this study was to compare energy expenditure (EE) estimates from 5 consumer physical activity monitors (PAMs) to indirect calorimetry in a sample of youth. Eighty-nine youth (mean (SD); age, 12.3 (3.4) years; 50% female) performed 16 semi-structured activities. Activities were performed in duplicate across 2 visits. Participants wore a Cosmed K4b 2 (criterion for EE), an Apple Watch 2 (left wrist), Mymo Tracker (right hip), and Misfit Shine 2 devices (right hip; right shoe). Participants were randomized to wear a Samsung Gear Fit 2 or a Fitbit Charge 2 on the right wrist. Oxygen consumption was converted to EE by subtracting estimated basal EE (Schofield's equation) from the measured gross EE. EE from each visit was summed across the 2 visit days for comparison with the total EE recorded from the PAMs. All consumer PAMs estimated gross EE, except for the Apple Watch 2 (net Active EE). Paired t tests were used to assess differences between estimated (PAM) and measured (K4b 2 ) EE. Mean absolute percent error (MAPE) was used to assess individual-level error. The Mymo Tracker was not significantly different from measured EE and was within 15.9 kcal of measured kilocalories ( p = 0.764). Mean percent errors ranged from 3.5% (Mymo Tracker) to 48.2% (Apple Watch 2). MAPE ranged from 16.8% (Misfit Shine 2 - right hip) to 49.9% (Mymo Tracker). Novelty Only the Mymo Tracker was not significantly different from measured EE but had the greatest individual error. The Misfit Shine 2 - right hip had the lowest individual error. Caution is warranted when using consumer PAMs in youth for tracking EE.

Purpose: The purpose of this study was to assess the accuracy of the Cosmed K5 portable metabolic system dynamic mixing chamber (MC) and breath-by-breath (BxB) modes against the criterion Douglas bag (DB) method., Methods: Fifteen participants (mean age±SD, 30.6±7.4 yrs) had their metabolic variables measured at rest and during cycling at 50, 100, 150, 200, and 250W. During each stage, participants were connected to the first respiratory gas collection method (randomized) for the first four minutes to reach steady state, followed by 3-min (or 5-min for DB) collection periods for the resting condition, and 2-min collection periods for all cycling intensities. Collection periods for the second and third methods were preceded by a washout of 1-3 min. Repeated measures ANOVAs were used to compare metabolic variables measured by each method, for seated rest and each cycling work rate., Results: For ventilation (VE) and oxygen uptake (VO2), the K5 MC and BxB modes were within 2.1 l/min (VE) and 0.08 l/min (VO2) of the DB (p≥0.05). Compared to DB values, carbon dioxide production (VCO2) was significantly underestimated by the K5 BxB mode at work rates ≥150W by 0.12-0.31 l/min (p<0.05). K5 MC and BxB respiratory exchange ratio values were significantly lower than DB at cycling work rates ≥100W by 0.03-0.08 (p<0.05)., Conclusion: Compared to the DB method, the K5 MC and BxB modes are acceptable for measuring VE and VO2 across a wide range of cycling intensities. Both K5 modes provided comparable values to each other., Competing Interests: The authors have declared that no competing interests exist.

The purpose of the study was to conduct a comprehensive evaluation of the ActiGraph GT3X+ (AG) and activPAL (AP) for assessing time spent in sedentary behaviors (SB) in youth using structured and free-living activities. Forty-four participants (mean±SD; age, 12.7±0.8 yrs) completed up to 8 structured activities and approximately 2-hrs of free-living activity while wearing an AG (right hip) and AP (right thigh). A Cosmed K4b 2 was used for measured energy expenditure (MET y ; activity VO 2 divided by resting VO 2 ). Direct observation was used during the structured activities. SB time was estimated using the inclinometer function of the AP and AG, and count thresholds with AG (<75 vector magnitude (VM) counts/10-s, < 25 vertical axis (VA) counts/10-s, and <50, 100, 150, and 200 VA counts/min). For the structured activities, the AG inclinometer and AP correctly classified supine rest about 45% of the time, seated activities 54.6% and 65.1% of the time, respectively, and walking and running >96% of the time. For the free-living measurement, the VA <25 counts/10-s has the lowest RMSE (20.6 min), while the VM <75 counts/10-s had the lowest MAPE (69.2%). The AG inclinometer was within 0.2 minutes of measured time, but had the highest MAPE (107.1%). The AP was within 1.6 minutes of measured time, but had the highest RMSE (28.5 minutes). Compared to measured SB time, the VA <25 counts/10-s and VM <75 counts/10-s provided the most precise estimates of SB during free-living activity. Further refinement is needed to improve the AP and AG posture estimates.

Purpose: The purpose of this study was to explore whether gyroscope and magnetometer data from the ActiGraph GT9X improved accelerometer-based predictions of energy expenditure (EE)., Methods: Thirty participants (mean ± SD: age, 23.0 ± 2.3 yr; body mass index, 25.2 ± 3.9 kg·m) volunteered to complete the study. Participants wore five GT9X monitors (right hip, both wrists, and both ankles) while performing 10 activities ranging from rest to running. A Cosmed K4b was worn during the trial, as a criterion measure of EE (30-s averages) expressed in METs. Triaxial accelerometer data (80 Hz) were converted to milli-G using Euclidean norm minus one (ENMO; 1-s epochs). Gyroscope data (100 Hz) were expressed as a vector magnitude (GVM) in degrees per second (1-s epochs) and magnetometer data (100 Hz) were expressed as direction changes per 5 s. Minutes 4-6 of each activity were used for analysis. Three two-regression algorithms were developed for each wear location: 1) ENMO, 2) ENMO and GVM, and 3) ENMO, GVM, and direction changes. Leave-one-participant-out cross-validation was used to evaluate the root mean square error (RMSE) and mean absolute percent error (MAPE) of each algorithm., Results: Adding gyroscope to accelerometer-only algorithms resulted in RMSE reductions between 0.0 METs (right wrist) and 0.17 METs (right ankle), and MAPE reductions between 0.1% (right wrist) and 6.0% (hip). When direction changes were added, RMSE changed by ≤0.03 METs and MAPE by ≤0.21%., Conclusions: The combined use of gyroscope and accelerometer at the hip and ankles improved individual-level prediction of EE compared with accelerometer only. For the wrists, adding gyroscope produced negligible changes. The magnetometer did not meaningfully improve estimates for any algorithms.

Step counting has long been used as a method of measuring distance. Starting in the mid-1900s, researchers became interested in using steps per day to quantify ambulatory physical activity. This line of research gained momentum after 1995, with the introduction of reasonably accurate spring-levered pedometers with digital displays. Since 2010, the use of accelerometer-based "activity trackers" by private citizens has skyrocketed. Steps have several advantages as a metric for assessing physical activity: they are intuitive, easy to measure, objective, and they represent a fundamental unit of human ambulatory activity. However, since they measure a human behavior, they have inherent biological variability; this means that measurements must be made over 3-7 days to attain valid and reliable estimates. There are many different kinds of step counters, designed to be worn on various sites on the body; all of these devices have strengths and limitations. In cross-sectional studies, strong associations between steps per day and health variables have been documented. Currently, at least eight prospective, longitudinal studies using accelerometers are being conducted that may help to establish dose-response relationships between steps/day and health outcomes. Longitudinal interventions using step counters have shown that they can help inactive individuals to increase by 2500 steps per day. Step counting is useful for surveillance, and studies have been conducted in a number of countries around the world. Future challenges include the need to establish testing protocols and accuracy standards, and to decide upon the best placement sites. These challenges should be addressed in order to achieve harmonization between studies, and to accurately quantify dose-response relationships.

Introduction: The purpose of this study was two-fold: 1) to investigate effects of cadence and sensitivity settings for the StepWatch (SW3) on step count accuracy over a wide range of ambulatory speeds, and 2) to compare the preprogrammed "quick start" settings to modified settings during intermittent lifestyle activities., Methods: Part 1: Fifteen participants (18-57 years of age) performed two trials of treadmill walking and running at ten speeds ranging from 26.8 to 268mmin -1 while wearing four SW3 devices. During the first trial, the cadence setting was maintained while sensitivity was varied; in the second trial sensitivity was maintained while the cadence setting was varied. Part 2: Fifteen participants performed four intermittent activities and drove an automobile while wearing two SW3 devices, one with preprogrammed settings and the other with the modified settings determined in Part 1., Results: Part 1: The modified settings (cadence setting of 70% of default and sensitivity of 16) provided the greatest step counting accuracy across a wide range of speeds reporting 96.0-104% of actual steps between 53.6 and 268mmin -1 . Part 2: The preprogrammed settings tended to have higher accuracy for light household tasks (recording 88% to 94% of actual steps) than the modified settings (recording 82% to 86% of actual steps) which showed a trend towards higher accuracy for tennis (recording 93% vs. 89% of actual steps) (p<0.05)., Conclusion: The preprogrammed "quick start" StepWatch settings should be used with individuals who do not engage in running and vigorous sports. However, for individuals who engage in running and tennis, use of modified settings may result in improved step counting accuracy., (Copyright © 2016 Elsevier B.V. All rights reserved.)