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

Methods to Assess Energy Expenditure of Resistance Exercise: A Systematic Scoping Review

BACKGROUND

Nutrition guidance for athletes must consider a range of variables to effectively support individuals in meeting energy and nutrient needs. Resistance exercise is a widely adopted training method in athlete preparation and rehabilitation and therefore is one such variable that will influence nutrition guidance. Given its prominence, the capacity to meaningfully quantify resistance exercise energy expenditure will assist practitioners and researchers in providing nutrition guidance. However, the significant contribution of anaerobic metabolism makes quantifying energy expenditure of resistance exercise challenging.

OBJECTIVE

The aim of this scoping review was to investigate the methods used to assess resistance exercise energy expenditure.

METHODS

A literature search of Medline, SPORTDiscus, CINAHL and Web of Science identified studies that included an assessment of resistance exercise energy expenditure. Quality appraisal of included studies was performed using the Rosendal Scale.

RESULTS

A total of 19,867 studies were identified, with 166 included after screening. Methods to assess energy expenditure included indirect calorimetry (n = 136), blood lactate analysis (n = 25), wearable monitors (n = 31) and metabolic equivalents (n = 4). Post-exercise energy expenditure was measured in 76 studies. The reported energy expenditure values varied widely between studies.

CONCLUSIONS

Indirect calorimetry is widely used to estimate energy expenditure. However, given its limitations in quantifying glycolytic contribution, indirect calorimetry during and immediately following exercise combined with measures of blood lactate are likely required to better quantify total energy expenditure. Due to the cumbersome equipment and technical expertise required, though, along with the physical restrictions the equipment places on participants performing particular resistance exercises, indirect calorimetry is likely impractical for use outside of the laboratory setting, where metabolic equivalents may be a more appropriate method.

Comparison of energy requirement estimation using activity record or accelerometer with doubly labeled water method in collegiate male sprinters

BACKGROUND & AIMS

Track and field sprinters must obtain an optimal body composition to improve sprint performance. To successfully change body composition, it is important to evaluate the estimated energy requirements (EER) and fluctuations in total energy expenditure (TEE). However, methods to accurately evaluate the EER and TEE in sprinters have not been fully investigated. The aim of this study was to compare currently used methods with the doubly labeled water (DLW) method, which is currently the gold standard for evaluating EER and TEE.

METHODS

Ten male collegiate sprinters participated in the study. We evaluated TEEDLW and compared it with the EER calculated using two equations used by the National Institute of Health and Nutrition (NIHN) and the Japan Institute of Sports Sciences (JISS). In addition, we evaluated the TEE from the activity record (AR) and triaxial accelerometer (ACC).

RESULTS

TEEDLW (3172 ± 415 kcal/day) was not significantly different from EERNIHN (p = 0.076) or EERJISS (p = 0.967). In addition, there were no significant differences between TEEDLW and TEEAR (p = 0.218). However, two accelerometer-derived equations used to evaluate TEE were found to have underestimated (2783 ± 377 kcal/day, p < 0.001) and overestimated (3405 ± 369 kcal/day, p = 0.009) the TEE.

CONCLUSION

Our results suggest that EERNIHN and EERJISS may be useful in evaluating the EER of collegiate male sprinters on a group basis, and AR may be more accurate than ACC in evaluating the TEE. These results may be helpful when considering nutritional support for male collegiate sprinters.

Individually optimized estimation of energy expenditure in rescue workers using a tri-axial accelerometer and heart rate monitor

Objectives: This study aimed to provide an improved energy expenditure estimation for heavy-load physical labor using accelerometer data and heart rate (HR) measured by wearables and to support food preparation and supply management for disaster relief and rescue operations as an expedition team. Methods: To achieve an individually optimized estimation for energy expenditure, a model equation parameter was determined based on the measurements of physical activity and HR during simulated rescue operations. The metabolic equivalent of task (MET), which was measured by using a tri-axial accelerometer and individual HR, was used, where two (minimum and maximum) or three (minimum, intermediate, and maximum) representative reference points were selected for each individual model fitting. In demonstrating the applicability of our approach in a realistic situation, accelerometer-based METs and HR of 30 males were measured using the tri-axial accelerometer and wearable HR during simulated rescue operations over 2 days. Results: Data sets of 27 rescue operations (age:34.2 ± 7.5 years; body mass index (BMI):22.9 ± 1.5 kg/m2) were used for the energy expenditure estimation after excluding three rescue workers due to their activity type and insufficient HR measurement. Using the combined approach with a tri-axial accelerometer and HR, the total energy expenditure increased by 143% for two points and 133% for three points, compared with the estimated total energy expenditure using only the accelerometer-based method. Conclusion: The use of wearables provided a reasonable estimation of energy expenditure for physical workers with heavy equipment. The application of our approach to disaster relief and rescue operations can provide important insights into nutrition and healthcare management.

Physical activity, residential greenness, and cardiac autonomic function

PURPOSE

This population-based study examines the associations between physical activity (PA), residential environmental greenness, and cardiac health measured by resting short-term heart rate variability (HRV).

METHODS

Residential greenness of a birth cohort sample (n = 5433) at 46 years was measured with normalized difference vegetation index (NDVI) by fixing a 1 km buffer around each participant's home. Daily light PA (LPA), moderate PA (MPA), vigorous PA (VPA), and the combination of both (MVPA) were measured using a wrist-worn accelerometer for 14 days. Resting HRV was measured with a heart rate monitor, and generalized additive modeling (GAM) was used to examine the association between PA, NDVI, and resting HRV.

RESULTS

In nongreen areas, men had less PA at all intensity levels compared to men in green areas. Women had more LPA and total PA and less MPA, MVPA, and VPA in green residential areas compared to nongreen areas. In green residential areas, men had more MPA, MVPA, and VPA than women, whereas women had more LPA than men. GAM showed positive linear associations between LPA, MVPA and HRV in all models.

CONCLUSIONS

Higher LPA and MVPA were significantly associated with increased HRV, irrespective of residential greenness. Greenness was positively associated with PA at all intensity levels in men, whereas in women, a positive association was found for LPA and total PA. A positive relationship of PA with resting HRV and greenness with PA was found. Residential greenness for promoting PA and heart health in adults should be considered in city planning.

Validation of Polar Grit X Pro for Estimating Energy Expenditure during Military Field Training: A Pilot Study

Wearables are lightweight, portable technology devices that are traditionally used to monitor physical activity and workload as well as basic physiological parameters such as heart rate. However recent advances in monitors have enabled better algorithms for estimation of caloric expenditure from heart rate for use in weight loss as well as sport performance. can be used for estimating energy expenditure and nutritional demand. Recently, the military has adopted the use of personal wearables for utilization in field studies for ecological validity of training. With popularity of use, the need for validation of these devices for caloric estimates is needed to assist in work-rest cycles. Thus the purpose of this effort was to evaluate the Polar Grit X for energy expenditure (EE) for use in military training exercises. Polar Grit X Pro watches were worn by active-duty elite male operators (N = 16; age: 31.7 ± 5.0 years, height: 180.1 ± 6.2 cm, weight: 91.7 ± 9.4 kg). Metrics were measured against indirect calorimetry of a metabolic cart and heart rate via a Polar heart rate monitor chest strap while exercising on a treadmill. Participants each performed five 10-minute bouts of running at a self-selected speed and incline to maintain a heart rate within one of five heart rate zones, as ordered and defined by Polar. Polar Grit X Pro watch had a good to excellent interrater reliability to indirect calorimetry at estimating energy expenditure (ICC = 0.8, 95% CI = 0.61-0.89, F (74,17.3) = 11.76, p < 0.0001) and a fair to good interrater reliability in estimating macronutrient partitioning (ICC = 0.49, 95% CI = 0.3-0.65, F (74,74.54) = 2.98, p < 0.0001). There is a strong relationship between energy expenditure as estimated from the Polar Grit X Pro and measured through indirect calorimetry. The Polar Grit X Pro watch is a suitable tool for estimating energy expenditure in free-living participants in a field setting and at a range of exercise intensities.

Monitoring Resistance Training in Real Time with Wearable Technology: Current Applications and Future Directions

Resistance training is an exercise modality that involves using weights or resistance to strengthen and tone muscles. It has become popular in recent years, with numerous people including it in their fitness routines to ameliorate their strength, muscle mass, and overall health. Still, resistance training can be complex, requiring careful planning and execution to avoid injury and achieve satisfactory results. Wearable technology has emerged as a promising tool for resistance training, as it allows monitoring and adjusting training programs in real time. Several wearable devices are currently available, such as smart watches, fitness trackers, and other sensors that can yield detailed physiological and biomechanical information. In resistance training research, this information can be used to assess the effectiveness of training programs and identify areas for improvement. Wearable technology has the potential to revolutionize resistance training research, providing new insights and opportunities for developing optimized training programs. This review examines the types of wearables commonly used in resistance training research, their applications in monitoring and optimizing training programs, and the potential limitations and challenges associated with their use. Finally, it discusses future research directions, including the development of advanced wearable technologies and the integration of artificial intelligence in resistance training research.

Validity of three smartwatches in estimating energy expenditure during outdoor walking and running

Commercially wrist-worn devices often present inaccurate estimations of energy expenditure (EE), with large between-device differences. We aimed to assess the validity of the Apple Watch Series 6 (AW), Garmin FENIX 6 (GF) and Huawei Watch GT 2e (HW) in estimating EE during outdoor walking and running. Twenty young normal-weight Chinese adults concurrently wore three index devices randomly positioned at both wrists during walking at 6 km/h and running at 10 km/h for 2 km on a 400- meter track. As a criterion, EE was assessed by indirect calorimetry (COSMED K5). For walking, EE from AW and GF was significantly higher than that obtained by the K5 (p < 0.001 and 0.002, respectively), but not for HW (p = 0.491). The mean absolute percentage error (MAPE) was 19.8% for AW, 32.0% for GF, and 9.9% for HW, respectively. The limits of agreement (LoA) were 44.1, 150.1 and 48.6 kcal for AW, GF, and HW respectively. The intraclass correlation coefficient (ICC) was 0.821, 0.216 and 0.760 for AW, GF, and HW, respectively. For running, EE from AW and GF were significantly higher than the K5 (p < 0.001 and 0.001, respectively), but not for HW (p = 0.946). The MAPE was 24.4%, 21.8% and 11.9% for AW, GF and HW, respectively. LoA were 62.8, 89.4 and 65.6 kcal for AW, GF and HW, respectively. The ICC was 0.741, 0.594, and 0.698 for AW, GF and HW, respectively. The results indicate that the tested smartwatches show a moderate validity in EE estimations for outdoor walking and running.

A multidevice and multimodal dataset for human energy expenditure estimation using wearable devices

We present a multi-device and multi-modal dataset, called WEEE, collected from 17 participants while they were performing different physical activities. WEEE contains: (1) sensor data collected using seven wearable devices placed on four body locations (head, ear, chest, and wrist); (2) respiratory data collected with an indirect calorimeter serving as ground-truth information; (3) demographics and body composition data (e.g., fat percentage); (4) intensity level and type of physical activities, along with their corresponding metabolic equivalent of task (MET) values; and (5) answers to questionnaires about participants' physical activity level, diet, stress and sleep. Thanks to the diversity of sensors and body locations, we believe that the dataset will enable the development of novel human energy expenditure (EE) estimation techniques for a diverse set of application scenarios. EE refers to the amount of energy an individual uses to maintain body functions and as a result of physical activity. A reliable estimate of people's EE thus enables computing systems to make inferences about users' physical activity and help them promoting a healthier lifestyle.

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.

Accelerometer-measured physical activity is associated with knee breadth in middle-aged Finns - a population-based study

Background

Articular surface size is traditionally considered to be a relatively stable trait throughout adulthood. Increased joint size reduces bone and cartilage tissue strains. Although physical activity (PA) has a clear association with diaphyseal morphology, the association between PA and articular surface size is yet to be confirmed. This cross-sectional study aimed to clarify the role of moderate-to-vigorous PA (MVPA) in knee morphology in terms of tibiofemoral joint size.

Methods

A sample of 1508 individuals from the population-based Northern Finland Birth Cohort 1966 was used. At the age of 46, wrist-worn accelerometers were used to monitor MVPA (≥3.5 METs) during a period of two weeks, and knee radiographs were used to obtain three knee breadth measurements (femoral biepicondylar breadth, mediolateral breadth of femoral condyles, mediolateral breadth of the tibial plateau). The association between MVPA and knee breadth was analyzed using general linear models with adjustments for body mass index, smoking, education years, and accelerometer weartime.

Results

Of the sample, 54.8% were women. Most individuals were non-smokers (54.6%) and had 9—12 years of education (69.6%). Mean body mass index was 26.2 (standard deviation 4.3) kg/m². MVPA was uniformly associated with all three knee breadth measurements among both women and men. For each 60 minutes/day of MVPA, the knee breadth dimensions were 1.8—2.0% (or 1.26—1.42 mm) larger among women (p < 0.001) and 1.4—1.6% (or 1.21—1.28 mm) larger among men (p < 0.001).

Conclusions

Higher MVPA is associated with larger tibiofemoral joint size. Our findings indicate that MVPA could potentially increase knee dimensions through similar biomechanical mechanisms it affects diaphyseal morphology, thus offering a potential target in reducing tissue strains and preventing knee problems. Further studies are needed to confirm and investigate the association between articulation area and musculoskeletal health.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-05475-7.

Steeper or Faster? Tactical Dispositions to Minimize Oxygen Cost in Ski Mountaineering

Purpose

Investigate the effect of speed, inclination, and use of heel elevator on the oxygen cost of vertical climbing (C_vert) in ski mountaineering.

Methods

In this study, 19 participants who were (3 women and 16 men) moderate- to well-trained recreational Norwegian ski mountaineers were involved. All participants were tested for VO_2max in running, and in a ski mountaineering test on a treadmill, to assess C_vert. The test protocol consisted of 12 4 min work periods at different inclinations from 13 to 23°, with continuous VO₂ measurements. After every second work period, the inclination increased by 2°, and speed was decreased accordingly. The speed reduction was based on the equation V_vert = speed · sin(α), where α represents the angle of inclination. V_vert was thus held constant for each work period (854 m·h⁻¹). All work periods were completed twice, with and without a heel elevator. Half of the subjects started with the smallest inclination, and the other half started with the steepest inclination.

Results

The results showed that C_vert was unchanged at all inclinations except 13°, where there was a significantly higher C_vert, at the same V_vert. Only at 13°, C_vert was higher with the use of heel elevator. There was also a significant trend indicating lower C_vert with use of heel elevator with steeper inclination.

Conclusions

There seemed to be nothing to gain by choosing detours if the inclination was 13° or less. The use of heel elevator was more advantageous, the steeper the inclination, but at 13° there was a negative effect of using heel elevator.

Recommendations for Determining the Validity of Consumer Wearables and Smartphones for the Estimation of Energy Expenditure: Expert Statement and Checklist of the INTERLIVE Network

BACKGROUND

Consumer wearables and smartphone devices commonly offer an estimate of energy expenditure (EE) to assist in the objective monitoring of physical activity to the general population. Alongside consumers, healthcare professionals and researchers are seeking to utilise these devices for the monitoring of training and improving human health. However, the methods of validation and reporting of EE estimation in these devices lacks rigour, negatively impacting on the ability to make comparisons between devices and provide transparent accuracy.

OBJECTIVES

The Towards Intelligent Health and Well-Being Network of Physical Activity Assessment (INTERLIVE) is a joint European initiative of six universities and one industrial partner. The network was founded in 2019 and strives towards developing best-practice recommendations for evaluating the validity of consumer wearables and smartphones. This expert statement presents a best-practice validation protocol for consumer wearables and smartphones in the estimation of EE.

METHODS

The recommendations were developed through (1) a systematic literature review; (2) an unstructured review of the wider literature discussing the potential factors that may introduce bias during validation studies; and (3) evidence-informed expert opinions from members of the INTERLIVE network.

RESULTS

The systematic literature review process identified 1645 potential articles, of which 62 were deemed eligible for the final dataset. Based on these studies and the wider literature search, a validation framework is proposed encompassing six key domains for validation: the target population, criterion measure, index measure, testing conditions, data processing and the statistical analysis.

CONCLUSIONS

The INTERLIVE network recommends that the proposed protocol, and checklists provided, are used to standardise the testing and reporting of the validation of any consumer wearable or smartphone device to estimate EE. This in turn will maximise the potential utility of these technologies for clinicians, researchers, consumers, and manufacturers/developers, while ensuring transparency, comparability, and replicability in validation.

TRIAL REGISTRATION

PROSPERO ID: CRD42021223508.

Lifestyle Modification Using a Wearable Biometric Ring and Guided Feedback Improve Sleep and Exercise Behaviors: A 12-Month Randomized, Placebo-Controlled Study

Purpose: Wearable biometric monitoring devices (WBMD) show promise as a cutting edge means to improve health and prevent disease through increasing accountability. By regularly providing real-time quantitative data regarding activity, sleep quality, and recovery, users may become more aware of the impact that their lifestyle has on their health. The purpose of this study was to examine the efficacy of a biometric tracking ring on improving sleep quality and increasing physical fitness over a one-year period. Methods: Fifty-six participants received a biometric tracking ring and were placed in one of two groups. One group received a 3-month interactive behavioral modification intervention (INT) that was delivered virtually via a smartphone app with guided text message feedback (GTF). The other received a 3-month non-directive wellness education control (CON). After three months, the INT group was divided into a long-term feedback group (LT-GTF) that continued to receive GTF for another nine months or short-term feedback group (ST-GTF) that stopped receiving GTF. Weight, body composition, and VO2max were assessed at baseline, 3months, and 12months for all participants and additionally at 6 and 9months for the ST-GTF and LT-GTF groups. To establish baseline measurements, sleep and physical activity data were collected daily over a 30-day period. Daily measurements were also conducted throughout the 12-month duration of the study. Results: Over the first 3months, the INT group had significant (p<0.001) improvements in sleep onset latency, daily step count, % time jogging, VO2max, body fat percentage, and heart rate variability (rMSSD HRV) compared to the CON group. Over the next 9months, the LT-GTF group continued to improve significantly (p<0.001) in sleep onset latency, daily step count, % time jogging, VO2max, and rMSSD HRV. The ST-GTF group neither improved nor regressed over the latter 9months except for a small increase in sleep latency. Conclusion: Using a WBMD concomitantly with personalized education, encouragement, and feedback, elicits greater change than using a WBMD alone. Additionally, the improvements achieved from a short duration of personalized coaching are largely maintained with the continued use of a WBMD.

Comparison of Energy Expenditure Assessed Using Wrist- and Hip-Worn ActiGraph GT3X in Free-Living Conditions in Young and Older Adults

The World Health Organization has presented their recommendations for energy expenditure to improve public health. Activity trackers do represent a modern solution for measuring physical activity, particularly in terms of steps/day and energy expended in physical activity (active energy expenditure). According to the manufacturer's instructions, these activity trackers can be placed on different body locations, mostly at the wrist and the hip, in an undifferentiated manner. The objective of this study was to compare the absolute error rate of active energy expenditure measured by a wrist-worn and hip-worn ActiGraph GT3X+ over a 24-h period in free-living conditions in young and older adults. Over the period of a 24-h period, 22 young adults and 22 older adults were asked to wear two ActiGraph GT3X+ at two different body locations recommended by the manufacturer, namely one around the wrist and one above the hip. Freedson algorithm was applied for data analysis. For both groups, the absolute error rate tended to decrease from 1,252 to 43% for older adults and from 408 to 46% for young participants with higher energy expenditure. Interestingly, for both young and older adults, the wrist-worn ActiGraph provided a significantly higher values of active energy expenditure (943 ± 264 cal/min) than the hip-worn (288 ± 181 cal/min). Taken together, these results suggest that caution is needed when using active energy expenditure as an activity tracker-based metric to quantify physical activity.

Descriptive Epidemiology of Interruptions to Free-Living Sitting Time in Middle-Age and Older Adults

National guidelines recommend physically active interruptions to sitting time, however, the characteristics of these interruptions are broadly stated and ill-defined. A robust methodology for population surveillance for such interruptions is needed.

PURPOSE

To describe the frequency and characteristics (i.e., duration, stepping time, and estimated intensity) of all interruptions and physically active interruptions to adults' free-living sitting time (i.e., transitions from sitting to upright posture) across segments of the population.

METHODS

Australian Diabetes, Obesity and Lifestyle (AusDiab) study participants (321 men; 406 women; mean ± SD 58.0 ± 10.3 years) wore the activPAL3TM for ≥1 valid day. The characteristics of interruptions from laboratory studies demonstrating health benefits were selected to define active interruptions (≥5 min upright and/or ≥ 2 min stepping) and ambulatory interruptions (≥2 min stepping). The frequency and characteristics of all, active, and ambulatory interruptions were described and compared by age, gender, diabetes status, and body mass index.

RESULTS

Adults averaged 55.0 ± 21.8 interruptions per day, but only 20.3 ± 6.7 were active and 14.0 ± 5.4 were ambulatory. Median (25th, 75th percentile) duration was 2.6 (0.9, 7.8) minutes, stepping time was 0.8 (0.3, 2.0) minutes, and estimated energy expenditure was 4.3 (1.4, 12.5) MET-min. Those who were older, had obesity, or had diabetes had significantly (p < 0.05) fewer interruptions of all types and less stepping time during active interruptions than their counterparts (Cohen's d < 0.2).

CONCLUSION

Free-living interruptions were often less active than interruptions performed in effective acute laboratory studies and their content varied widely between population groups. Monitoring all interruptions as well as those that are more active is advisable to provide a comprehensive understanding of free-living sedentary behavior.

Infant motor development and physical activity and sedentary time at midlife

This study investigated whether the timing of infant motor development is associated with self-reported and accelerometer-measured physical activity (PA) and sedentary time (ST) in midlife. This population-based study consisted of 4098 people born in 1966 in Northern Finland (NFBC 1966). Data on nine infant motor developmental milestones included making sounds, holding up the head, grabbing objects, turning from back to tummy, sitting without support, standing with support, walking with support, standing without support, and walking without support. At the age of 46, PA at leisure time and sitting time was self-reported. PA and ST were also measured with a wrist-worn Polar Active accelerometer that was instructed to be worn on the non-dominant hand 24 h/d for 14 days. A multiple linear regression analysis was used to analyze the association between infant motor development and PA and ST in midlife. Later infant motor development was weakly associated with higher accelerometer-measured light PA, but not with moderate-to-vigorous PA. Later infant locomotor development was associated with lower accelerometer-measured ST (β -0.07, p = 0.012) and lower self-reported sitting time at work (β -0.06, p = 0.004) in women. In conclusion, later infant motor development was associated with higher light PA and lower sedentary time at middle age. PA is a multifactorial behavior influenced by various factors from early childhood to midlife. Further research is required before more general conclusions can be drawn.

Casein and Whey Protein in the Breast Milk Ratio: Could It Promote Protein Metabolism Enhancement in Physically Active Adults?

Due to the utilization of milk proteins such as whey protein (WP) and casein as sports nutrition ergogenic aids, the present study investigated the effects of the association of WP and casein in a ratio of 80:20, a similar ratio of human breast milk, on blood branched-chain amino acid (BCAA) profiles, markers of protein metabolism and delayed onset muscle soreness (DOMS), after a single bout of resistance exercise. A double-blind, crossover and acute study was carried out with ten men (age 29 ± 8 years; BMI: 25.4 ± 2.9 kg/m²; 77 ± 12 kg; 1.74 ± 0.09 m); each one consumed the following supplements randomly, one per session: WP, CAS (casein), WP/CAS (80% WP/20% CAS), CAS/WP (80% CAS/20% WP) and PLA (placebo). They were also subjected to the following evaluations: the one repetition maximum (1RM) test; resistance training session; blood extraction during each session to determine the BCAA profile; two food records; 3-day evaluation of DOMS (24 h, 48 h and 72 h) and nitrogen balance in each treatment. The intervention resulted in similar nitrogen urinary, creatinine and urea plasma levels and showed a positive nitrogen balance in all the trials. Regarding the BCAAs, the peak occurred at 60 min post-ingestion and remained higher until 120 min for WP, WP/CAS and CAS/WP. The DOMS was significantly lower for WP, WP/CAS and CAS/WP compared to the CAS and PLA treatments. There were no advantages in the association of WP and CAS in the BCAAs profile when compared to WP itself, but it induced a lower DOMS compared to CAS and PLA (Clinical Trial registration number: clinicaltrials.gov, NCT04648384).

Age Differences in Estimating Physical Activity by Wrist Accelerometry Using Machine Learning

Accelerometer-based fitness trackers and smartwatches are proliferating with incessant attention towards health tracking. Despite their growing popularity, accurately measuring hallmark measures of physical activities has yet to be accomplished in adults of all ages. In this work, we evaluated the performance of four machine learning models: decision tree, random forest, extreme gradient boosting (XGBoost) and least absolute shrinkage and selection operator (LASSO), to estimate the hallmark measures of physical activities in young (20-50 years), middle-aged (50-70 years], and older adults (70-89 years]. Our models were built to recognize physical activity types, recognize physical activity intensities, estimate energy expenditure (EE) and recognize individual physical activities using wrist-worn tri-axial accelerometer data (33 activities per participant) from a large sample of participants (n = 253, 62% women, aged 20-89 years old). Results showed that the machine learning models were quite accurate at recognizing physical activity type and intensity and estimating energy expenditure. However, models performed less optimally when recognizing individual physical activities. F1-Scores derived from XGBoost's models were high for sedentary (0.955-0.973), locomotion (0.942-0.964) and lifestyle (0.913-0.949) activity types with no apparent difference across age groups. Low (0.919-0.947), light (0.813-0.828) and moderate (0.846-0.875) physical activity intensities were also recognized accurately. The root mean square error range for EE was approximately 1 equivalent of resting EE [0.835-1.009 METs]. Generally, random forest and XGBoost models outperformed other models. In conclusion, machine learning models to label physical activity types, activity intensity and energy expenditure are accurate and there are minimal differences in their performance across young, middle-aged and older adults.

Factors associated with validity of consumer-oriented wearable physical activity trackers: a meta-analysis

The purposes of this study were to examine (1) the strength of the criterion validity evidence of various consumer-oriented wearable physical activity trackers, (2) the influence of brands of consumer-oriented wearable physical activity on validity evidence and (3) factors that may contribute to differences in the strength of the criterion validity evidence. A total of 589 articles were identified through four databases. Pairs of researchers reviewed the articles to determine eligibility. A total of 29 studies with 96 validity coefficients were included in the meta-analysis. Five different moderators, including the brands of physical activity trackers, placement of devices, type of activities (ambulatory vs. lifestyle activities), population, and release year, were analysed to examine which factors impact the validity evidence. The summarised validity coefficient between activity trackers and energy expenditure ranged from r = .41 to r = .91. Moderator analyses revealed that the brand, placement of the device, and population significantly impact the magnitude of the validity evidence, while the type of activity and release year of the devices do not. Device brand, population, andplacement are each factor that significantly affects the validity coefficientsbetween consumer-oriented wearable physical activity trackers. Efforts should be made to improve the accuracy of these devices to maintain the credibility of the research and the trust of consumers.

Using Accelerometry for Evaluating Energy Consumption and Running Intensity Distribution Throughout a Marathon According to Sex

The proportion of females participating in long-distance races has been increasing in the last years. Although it is well-known that there are differences in how females and males face a marathon, higher research may be done to fully understand the intrinsic and extrinsic factors affecting sex differences in endurance performance. In this work, we used triaxial accelerometer devices to monitor 74 males and 14 females, aged 30 to 45 years, who finished the Valencia Marathon in 2016. Moreover, marathon split times were provided by organizers. Several physiological traits and training habits were collected from each participant. Then, we evaluated several accelerometry- and pace-estimated parameters (pacing, average change of speed, energy consumption, oxygen uptake, running intensity distribution and running economy) in female and male amateur runners. In general, our results showed that females maintained a more stable pacing and ran at less demanding intensity throughout the marathon, limiting the decay of running pace in the last part of the race. In fact, females ran at 4.5% faster pace than males in the last kilometers. Besides, their running economy was higher than males (consumed nearly 19% less relative energy per distance) in the last section of the marathon. Our results may reflect well-known sex differences in physiology (i.e., muscle strength, fat metabolism, VO_2max), and in running strategy approach (i.e., females run at a more conservative intensity level in the first part of the marathon compared to males). The use of accelerometer devices allows coaches and scientific community to constantly monitor a runner throughout the marathon, as well as during training sessions.

Correlates of physical activity behavior in adults: a data mining approach

PURPOSE

A data mining approach was applied to establish a multilevel hierarchy predicting physical activity (PA) behavior, and to methodologically identify the correlates of PA behavior.

METHODS

Cross-sectional data from the population-based Northern Finland Birth Cohort 1966 study, collected in the most recent follow-up at age 46, were used to create a hierarchy using the chi-square automatic interaction detection (CHAID) decision tree technique for predicting PA behavior. PA behavior is defined as active or inactive based on machine-learned activity profiles, which were previously created through a multidimensional (clustering) approach on continuous accelerometer-measured activity intensities in one week. The input variables (predictors) used for decision tree fitting consisted of individual, demographical, psychological, behavioral, environmental, and physical factors. Using generalized linear mixed models, we also analyzed how factors emerging from the model were associated with three PA metrics, including daily time (minutes per day) in sedentary (SED), light PA (LPA), and moderate-to-vigorous PA (MVPA), to assure the relative importance of methodologically identified factors.

RESULTS

Of the 4582 participants with valid accelerometer data at the latest follow-up, 2701 and 1881 had active and inactive profiles, respectively. We used a total of 168 factors as input variables to classify these two PA behaviors. Out of these 168 factors, the decision tree selected 36 factors of different domains from which 54 subgroups of participants were formed. The emerging factors from the model explained minutes per day in SED, LPA, and/or MVPA, including body fat percentage (SED: B = 26.5, LPA: B = - 16.1, and MVPA: B = - 11.7), normalized heart rate recovery 60 s after exercise (SED: B = -16.1, LPA: B = 9.9, and MVPA: B = 9.6), average weekday total sitting time (SED: B = 34.1, LPA: B = -25.3, and MVPA: B = -5.8), and extravagance score (SED: B = 6.3 and LPA: B = - 3.7).

CONCLUSIONS

Using data mining, we established a data-driven model composed of 36 different factors of relative importance from empirical data. This model may be used to identify subgroups for multilevel intervention allocation and design. Additionally, this study methodologically discovered an extensive set of factors that can be a basis for additional hypothesis testing in PA correlates research.

Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview

In the last few decades, a number of technological developments have advanced the spread of wearable sensors for the assessment of human motion. These sensors have been also developed to assess athletes' performance, providing useful guidelines for coaching, as well as for injury prevention. The data from these sensors provides key performance outcomes as well as more detailed kinematic, kinetic, and electromyographic data that provides insight into how the performance was obtained. From this perspective, inertial sensors, force sensors, and electromyography appear to be the most appropriate wearable sensors to use. Several studies were conducted to verify the feasibility of using wearable sensors for sport applications by using both commercially available and customized sensors. The present study seeks to provide an overview of sport biomechanics applications found from recent literature using wearable sensors, highlighting some information related to the used sensors and analysis methods. From the literature review results, it appears that inertial sensors are the most widespread sensors for assessing athletes' performance; however, there still exist applications for force sensors and electromyography in this context. The main sport assessed in the studies was running, even though the range of sports examined was quite high. The provided overview can be useful for researchers, athletes, and coaches to understand the technologies currently available for sport performance assessment.

Wrist-Worn Wearables for Monitoring Heart Rate and Energy Expenditure While Sitting or Performing Light-to-Vigorous Physical Activity: Validation Study

Background

Physical activity reduces the incidences of noncommunicable diseases, obesity, and mortality, but an inactive lifestyle is becoming increasingly common. Innovative approaches to monitor and promote physical activity are warranted. While individual monitoring of physical activity aids in the design of effective interventions to enhance physical activity, a basic prerequisite is that the monitoring devices exhibit high validity.

Objective

Our goal was to assess the validity of monitoring heart rate (HR) and energy expenditure (EE) while sitting or performing light-to-vigorous physical activity with 4 popular wrist-worn wearables (Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa).

Methods

While wearing the 4 different wearables, 25 individuals performed 5 minutes each of sitting, walking, and running at different velocities (ie, 1.1 m/s, 1.9 m/s, 2.7 m/s, 3.6 m/s, and 4.1 m/s), as well as intermittent sprints. HR and EE were compared to common criterion measures: Polar-H7 chest belt for HR and indirect calorimetry for EE.

Results

While monitoring HR at different exercise intensities, the standardized typical errors of the estimates were 0.09-0.62, 0.13-0.88, 0.62-1.24, and 0.47-1.94 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 0.9%-4.3%, 2.2%-6.7%, 2.9%-9.2%, and 4.1%-19.1%, respectively, for the 4 wearables. While monitoring EE at different exercise intensities, the standardized typical errors of the estimates were 0.34-1.84, 0.32-1.33, 0.46-4.86, and 0.41-1.65 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 13.5%-27.1%, 16.3%-28.0%, 15.9%-34.5%, and 8.0%-32.3%, respectively.

Conclusions

The Apple Watch Series 4 provides the highest validity (ie, smallest error rates) when measuring HR while sitting or performing light-to-vigorous physical activity, followed by the Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, in that order. The Apple Watch Series 4 and Polar Vantage V are suitable for valid HR measurements at the intensities tested, but HR data provided by the Garmin Fenix 5 and Fitbit Versa should be interpreted with caution due to higher error rates at certain intensities. None of the 4 wrist-worn wearables should be employed to monitor EE at the intensities and durations tested.

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

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

Healthy Eating, Physical Activity, and Sleep Hygiene (HEPAS) as the Winning Triad for Sustaining Physical and Mental Health in Patients at Risk for or with Neuropsychiatric Disorders: Considerations for Clinical Practice

Neuropsychiatric disorders stem from gene-environment interaction and their development can be, at least in some cases, prevented by the adoption of healthy and protective lifestyles. Once full blown, neuropsychiatric disorders are prevalent conditions that patients live with a great burden of disability. Indeed, the determinants that increase the affliction of neuropsychiatric disorders are various, with unhealthy lifestyles providing a significant contribution in the interplay between genetic, epigenetic, and environmental factors that ultimately represent the pathophysiological basis of these impairing conditions. On one hand, the adoption of Healthy Eating education, Physical Activity programs, and Sleep hygiene promotion (HEPAS) has the potential to become one of the most suitable interventions to reduce the risk to develop neuropsychiatric disorders, while, on the other hand, its integration with pharmacological and psychological therapies seems to be essential in the overall management of neuropsychiatric disorders in order to reduce the disability and improve the quality of life of affected patients. We present an overview of the current evidence in relation to HEPAS components in the prevention and management of neuropsychiatric disorders and provide suggestions for clinical practice.