Physical activity is important for health, but the influence of structured, supervised aerobic exercise sessions on habitual physical activity in healthy older adults is unclear.

We evaluated habitual physical activity in the Hertfordshire Physical Activity Trial, where healthy older adults were randomised to 36 supervised 1-hour gymnasium sessions on a cycle ergometer at moderate intensity over 12 weeks or to a control group with no intervention. We estimated physical activity energy expenditure (PAEE) and time spent in sedentary behaviour and light and moderate or vigorous physical activity over 7 days at three time points (before, during and immediately after the intervention) with individually calibrated combined heart rate and movement sensing.

Of 100 randomised participants (44% female, aged 67-76 years), 96% completed follow-up. Midway through the intervention, neither overall PAEE nor time spent at different intensities were different between groups. However, on the 3 days of the week that the structured exercise sessions occurred (Monday, Wednesday, Friday), the exercise group had a 9.1 kJ kg-1 day-1 ((2.5, 15.7), p=0.007) increase in PAEE, a reduction in sedentary time and increased time spent at light and moderate or vigorous physical activity, compared with the control group.

Three 1-hour bouts per week of structured aerobic exercise increased daily physical activity on the days they occurred, but not overall physical activity across the whole week. Population-wide strategies such as better cycling and walking infrastructure may increase physical activity in healthy older adults more effectively than treatment with structured exercise programmes.

ISRCTN60986572.

The ability to perform bodily movement varies in ageing men and women. We investigated whether physical fitness may explain sex differences in daily physical activity energy expenditure (PAEE) among older people.

In this cross-sectional study, a population-based cohort of 75, 80, and 85-year-old men and women (n = 409, 62% women) underwent laboratory-based assessment of walking speed, maximal knee extension strength, and body fat percentage. Free-living physical activity was assessed as total PAEE, and light (LPA) and moderate-to-vigorous physical activity (MVPA) using individually calibrated combined accelerometry and heart rate sensing. Path modelling was used to examine indirect associations between sex, physical fitness, and physical activity.

Men had better physical fitness and higher overall PAEE (mean 34.0 (SD 10.8) kJ/kg/day) than women (28.3 (8.4) kJ/kg/day, p<0.001). The path model for PAEE explained 33% of the variance. The direct association between sex and PAEE was non-significant, whereas the association between sex and PAEE mediated by body fat (β = 0.20, p<0.001) and walking speed (β = 0.05, p = 0.001) were statistically significant. Similarly, associations between sex and MVPA mediated by body fat (β = 0.11, p = 0.002) and walking speed (β = 0.05, p = 0.001) were significant, as were the associations between sex and LPA mediated by body fat (β = 0.24, p<0.001) and walking speed (β = 0.03, p = 0.019).

Differences in physical activity between men and women may reflect underlying differences in cardiorespiratory fitness and adiposity. These results highlight the importance of maintaining physical fitness to support active living in older adults.

Exercise is an essential component of body mass management interventions. Overnight-fasted exercise (FASTex) acutely enhances fat oxidation compared with fed exercise (FEDex). However, consistent FASTex training does not typically further enhance body mass loss, suggesting the induction of energy compensation responses. The present study aimed to test the effects of FASTex or FEDex on the components of energy balance (i.e., energy intake (EI), energy expenditure (EE), and appetite) and interstitial glucose metrics across four days.

Twelve (10 men, 2 women) healthy, physically active participants (age 22.6 + 1.2 years (mean ± SD); BMI 22.5 ± 2.8 kg ⋅ m-2) were studied twice, across four days, after a 75-min run either FASTex or FEDex. Daily EI was obtained after subtracting leftovers from the provided food. Daily fasting appetite was measured by visual analogue scales. Activity- and total- EE (AEE & TEE, respectively) were estimated by combining heart rate and accelerometry. Continuous glucose monitoring was used to capture daily interstitial glucose metrics and Likert scales were utilised to quantify fatigue, stress, sleep quality, and muscle soreness levels.

No differences between conditions were observed for EI (FASTex = 15.0 ± 0.1 vs FEDex = 15.0 ± 0.4 MJ⋅day-1; p = 0.865), AEE (FASTex = 7.6 ± 1.1 vs FEDex 7.8 ± 1.3 MJ⋅day-1; p = 0.223) and TEE (FASTex = 15.9 ± 3.4 vs 14.9 ± 4.5 MJ⋅day-1; p = 0.136). Additionally, no condition effects for appetite (p > 0.05) and interstitial glucose (p = 0.074) were observed.

FASTex did not differ from FEDex in the response of components of energy balance or interstitial glucose across four days, suggesting that both exercise approaches could be used interchangeably.

Limited observational evidence suggests that a higher intake of high-fat dairy may be associated with lower prediabetes risk, while opposite associations have been observed for low-fat milk intake. This study aimed to examine associations between baseline and changes in dairy consumption, risk of prediabetes, and glycaemic status.

7521 participants from the prospective UK Fenland study were included (mean age 48.7 ± 2.0 years, 51.9 % female). Dairy intake was measured using self-reported food frequency questionnaires. Associations with prediabetes risk and glycaemic status were analysed using Poisson regression models adjusted for social demographics, health behaviours, family history of diabetes and food group intake.

At a mean follow-up of 6.7 ± 2.0 years, 290 participants developed prediabetes (4.3 %). Most dairy products were not significantly associated with prediabetes risk. A higher baseline intake of high-fat dairy (RRservings/day 1.20, 95%CI 1.03-1.39) and high-fat milk (RRservings/day 1.22, 1.01-1.47) were associated with higher prediabetes risk. Conversely, low-fat milk was associated with lower prediabetes risk (RRservings/day 0.86, 0.75-0.98). In the analyses evaluating dietary changes over time, increases in high-fat milk were inversely associated with risk of progressing from normoglycaemia to prediabetes or type 2 diabetes (RRservings/day 0.86, 95%CI 0.75-0.99).

This population-based study showed that most dairy products are not associated with prediabetes risk or progression in glycaemic status. Positive associations of high-fat dairy, high-fat milk, and the inverse association of low-fat milk with prediabetes risk found were inconsistent with prior literature and suggestive of the need for future research on environmental, behavioural, and biological factors that explain the available evidence.

Broad-capture proteomic platforms now enable simultaneous assessment of thousands of plasma proteins, but most of these are not actively secreted and their origins are largely unknown. Here we integrate genomic with deep phenomic information to identify modifiable and non-modifiable factors associated with 4,775 plasma proteins in ~8,000 mostly healthy individuals. We create a data-driven map of biological influences on the human plasma proteome and demonstrate segregation of proteins into clusters based on major explanatory factors. For over a third (N = 1,575) of protein targets, joint genetic and non-genetic factors explain 10-77% of the variation in plasma (median 19.88%, interquartile range 14.01-31.09%), independent of technical factors (median 2.48%, interquartile range 0.78-6.41%). Together with genetically anchored causal inference methods, our map highlights potential causal associations between modifiable risk factors and plasma proteins for hundreds of protein-disease associations, for example, COL6A3, which possibly mediates the association between reduced kidney function and cardiovascular disease. We provide a map of biological and technical influences on the human plasma proteome to help contextualize findings from proteomic studies.

Diabetes is associated with impairments in muscle mass and quality increasing the risk of sarcopenia. Thus, this study aimed to investigate the odds of sarcopenia and its associated risk factors among Qatari adults (> 18 years), while exploring the modulating effects of health and lifestyle factors.

Using a case-control design, data from 767 participants (481 cases with diabetes and 286 controls without diabetes) was collected from Qatar Biobank (QBB). Sociodemographic, lifestyle factors including dietary intake, anthropometric and biochemical measures were analyzed. Handgrip strength, Dual X-ray absorptiometry (DXA), and Bio-impedance were used to assess muscle strength, muscle mass and muscle quality, respectively. The risk of sarcopenia was estimated using the European consensus on definition and diagnosis of sarcopenia.

Cases with diabetes were older (55 vs. 36 years; P < 0.001), had higher BMI (31.6 vs. 28.3 kg/m2; P < 0.001), lower cardiorespiratory fitness (50.0% "Moderate" fitness for cases, 62.9% "High" fitness for controls), and consumed less total (59.0 vs. 64.0; P = 0.004) and animal protein (39.0 vs. 42.0; P = 0.001), compared to controls based on a computed score. Participants with diabetes also had lower appendicular lean mass/BMI, handgrip strength, and higher probability of sarcopenia/probable sarcopenia (P < 0.005). Adjusted multiple logistic regression revealed that elevated cardiorespiratory fitness (β = 0.299, 95%CI:0.12-0.74) and blood triglycerides (β = 1.475, 95% CI: 1.024-2.124), as well as being a female (β = 0.086, 95%CI: 0.026-0.288) and having higher BMI (β = 0.908, 95%CI: 0.852-0.967) and ALM/BMI (β = 0.000, 95% CI: 0.000-0.007) are independent predictors (p < 0.05) of sarcopenia risk.

This study highlights the intricate relationship between diabetes and sarcopenia, revealing modifiable risk factors. Individuals with diabetes were found to have a higher likelihood of sarcopenia, which was associated with lower fitness levels and higher blood triglycerides. Protective factors against sarcopenia included being female and having higher BMI and ALM/BMI ratios.

Restricted sugar and ketogenic diets can alter energy balance/metabolism, but decreased energy intake may be compensated by reduced expenditure. In healthy adults, randomization to restricting free sugars or overall carbohydrates (ketogenic diet) for 12 weeks reduces fat mass without changing energy expenditure versus control. Free-sugar restriction minimally affects metabolism or gut microbiome but decreases low-density lipoprotein cholesterol (LDL-C). In contrast, a ketogenic diet decreases glucose tolerance, increases skeletal muscle PDK4, and reduces AMPK and GLUT4 levels. By week 4, the ketogenic diet reduces fasting glucose and increases apolipoprotein B, C-reactive protein, and postprandial glycerol concentrations. However, despite sustained ketosis, these effects are no longer apparent by week 12, when gut microbial beta diversity is altered, possibly reflective of longer-term adjustments to the ketogenic diet and/or energy balance. These data demonstrate that restricting free sugars or overall carbohydrates reduces energy intake without altering physical activity, but with divergent effects on glucose tolerance, lipoprotein profiles, and gut microbiome.

The Belgian Special Operations Regiment undergoes an 8-week basic training course (SOF Basic Course) following the Military Initiation Phase. The aims of the present study were to estimate energy expenditure and changes in body composition during SOF Basic Course.

A multi frequency body composition analyser assessed baseline and endpoint body composition. For the purpose of estimating energy expenditure, 41 participants were fitted with triaxial accelerometers. T-tests were performed on paired and unpaired samples in order to estimate statistical significance. Effect size was estimated with Cohen's d.

SOF Basic Course was completed by 88 out of 126 participants. The participants' mean (SD) age was 25.0 (4.1) years, weight was 77.6 (8.6) kg, and body fat percentage was 15.3% (3.3). Body weight of completers decreased from 78.3 (8.8) kg to 76.4 (8.0) kg (P = 0.01). Also, body fat decreased by 3.1 (1.8) kg (P = 0.01), and muscle mass increased by 1.2 (1.7) kg (P = 0.01). There was a decrease in body fat percentage from 15.3% (3.3) to 11.6% (3.4) (P = 0.01), with a Cohen's effect size of 1.86.The loss of 3.1 kg of body fat corresponds to a loss of 21,700.0 kcal (90.3 MJ) or 362.0 kcal.d-1 (1.5 MJ.d-1). The mean (SD) energy expenditure by physical activity was 1,943.0 (653.8) kcal.d-1 (14.6 [2.7] MJ.d-1). The average (SD) total energy expenditure was 4,088.0 (710.0) kcal.d-1 (36.8 [3.8] MJ.d-1).

Tactical athletes must perform in hypo-energetic environment. Research in the future should investigate the impact of increased energy intake on body composition.

Despite the wide effects of cardiorespiratory fitness (CRF) on metabolic, cardiovascular, pulmonary and neurological health, challenges in the feasibility and reproducibility of CRF measurements have impeded its use for clinical decision-making. Here we link proteomic profiles to CRF in 14,145 individuals across four international cohorts with diverse CRF ascertainment methods to establish, validate and characterize a proteomic CRF score. In a cohort of around 22,000 individuals in the UK Biobank, a proteomic CRF score was associated with a reduced risk of all-cause mortality (unadjusted hazard ratio 0.50 (95% confidence interval 0.48-0.52) per 1 s.d. increase). The proteomic CRF score was also associated with multisystem disease risk and provided risk reclassification and discrimination beyond clinical risk factors, as well as modulating high polygenic risk of certain diseases. Finally, we observed dynamicity of the proteomic CRF score in individuals who undertook a 20-week exercise training program and an association of the score with the degree of the effect of training on CRF, suggesting potential use of the score for personalization of exercise recommendations. These results indicate that population-based proteomics provides biologically relevant molecular readouts of CRF that are additive to genetic risk, potentially modifiable and clinically translatable.

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.

Increased physical activity (PA) may mitigate the negative cardiovascular health effects of sedentary behavior in adolescents. However, the relationship of PA and sedentary time from childhood with cardiac function in adolescence remains underexplored. Therefore, we investigated the associations of cumulative sedentary time and PA from childhood to adolescence with cardiac function in adolescence.

Participants were 153 adolescents (69 girls) who were aged 6 to 8 years at baseline, 8 to 10 years at 2-year follow-up, and 15 to 17 years at 8-year follow-up. Cumulative sedentary time and PA exposure between baseline and 2-year follow-up and between baseline and 8-year follow-up were measured using a combined accelerometer and heart rate monitor. Cardiac function was assessed using impedance cardiography at 8-year follow-up. The data were analyzed using linear regression analyses adjusted for age and sex. Cumulative moderate to vigorous PA (standardized regression coefficient [β]=-0.323 [95% CI, -0.527 to -0.119]) and vigorous PA (β=-0.295 [95% CI, -0.508 to -0.083]) from baseline to 8-year follow-up were inversely associated with cardiac work at 8-year follow-up. Conversely, cumulative sedentary time had a positive association (β=0.245 [95% CI, 0.092-0.398]). Cumulative vigorous PA from baseline to 8-year follow-up was inversely associated with cardiac work index at 8-year follow-up (β=-0.218 [95% CI, -0.436 to 0.000]).

Higher levels of sedentary time and lower levels of PA during childhood were associated with higher cardiac work in adolescence, highlighting the importance of increasing PA and reducing sedentary time from childhood.

This study aimed to assess whether moderate-to-vigorous physical activity (MVPA), sport and exercise as a proxy measure of muscle and bone strengthening activity, sedentary behaviour, and sleep were associated with total-body-less-head (TBLH) bone mineral content (BMC) and TBLH lean mass cross-sectionally and longitudinally from age 6 to 9 years and age 9 to 11 years to age 15 to 17 years.

We used longitudinal data from a population sample of Finnish children from the Physical Activity and Nutrition in Children study (age 6 to 9 years: n = 478, 229 females; age 9 to 11 years: n = 384, 197 females; age 15 to 17 years: n = 222, 103 females). Linear regression analysed the cross-sectional and longitudinal associations between accelerometer-assessed MVPA, sedentary time and sleep, and questionnaire-assessed sport and exercise participation and screen time with dual-energy X-ray absorptiometry-assessed TBLH BMC and lean mass.

In females, MVPA at age 6 to 9 years was positively associated with TBLH BMC at age 15 to 17 years (β = 0.008, p = 0.010). Sport and exercise at age 9 to 11 years was positively associated with TBLH BMC (β = 0.020, p = 0.002) and lean mass (β = 0.343, p = 0.040) at age 15 to 17 years. MVPA at age 9 to 11 years was positively associated with TBLH lean mass (β = 0.272, p = 0.004) at age 15 to 17 years. In males, sleep at age 6 to 9 years was positively associated with TBLH lean mass (β = 0.382, p = 0.003) at age 15 to 17 years. Sport and exercise at age 9 to 11 years was positively associated with TBLH BMC (β = 0.027, p = 0.012) and lean mass (β = 0.721, p < 0.001) at age 15 to 17 years.

Promoting engagement in the 24-hour movement behaviours in childhood, particularly sport and exercise to strengthen muscle and bone, is important in supporting bone and lean mass development in adolescence.

NCT01803776; first trial registration date: 04/03/2013.

Traditional jumping-dance rituals performed by Maasai men involve prolonged physical exertion that may contribute significantly to overall physical activity level. We aimed to objectively quantify the metabolic intensity of jumping-dance activity and assess associations with habitual physical activity and cardiorespiratory fitness (CRF).

Twenty Maasai men (18-37 years) from rural Tanzania volunteered to participate in the study. Habitual physical activity was monitored using combined heart rate (HR) and movement sensing over 3 days, and jumping-dance engagement was self-reported. A 1-h jumping-dance session resembling a traditional ritual was organized, during which participants' vertical acceleration and HR were monitored. An incremental, submaximal 8-min step test was performed to calibrate HR to physical activity energy expenditure (PAEE) and assess CRF.

Mean (range) habitual PAEE was 60 (37-116) kJ day-1  kg-1 , and CRF was 43 (32-54) mL O2  min-1  kg-1 . The jumping-dance activity was performed at an absolute HR of 122 (83-169) beats·min-1 , and PAEE of 283 (84-484) J min-1  kg-1 or 42 (18-75)% when expressed relative to CRF. The total PAEE for the session was 17 (range 5-29) kJ kg-1 , ~28% of the daily total. Self-reported engagement in habitual jumping-dance frequency was 3.8 (1-7) sessions/week, with a total duration of 2.1 (0.5-6.0) h/session.

Intensity during traditional jumping-dance activity was moderate, but on average sevenfold higher than habitual physical activity. These rituals are common, and can make a substantial contribution to overall physical activity in Maasai men, and thus be promoted as a culture-specific activity to increase energy expenditure and maintain good health in this population.

Higher cardiorespiratory fitness is associated with lower risk of type 2 diabetes. However, the causality of this relationship and the biological mechanisms that underlie it are unclear. Here, we examine genetic determinants of cardiorespiratory fitness in 450k European-ancestry individuals in UK Biobank, by leveraging the genetic overlap between fitness measured by an exercise test and resting heart rate. We identified 160 fitness-associated loci which we validated in an independent cohort, the Fenland study. Gene-based analyses prioritised candidate genes, such as CACNA1C, SCN10A, MYH11 and MYH6, that are enriched in biological processes related to cardiac muscle development and muscle contractility. In a Mendelian Randomisation framework, we demonstrate that higher genetically predicted fitness is causally associated with lower risk of type 2 diabetes independent of adiposity. Integration with proteomic data identified N-terminal pro B-type natriuretic peptide, hepatocyte growth factor-like protein and sex hormone-binding globulin as potential mediators of this relationship. Collectively, our findings provide insights into the biological mechanisms underpinning cardiorespiratory fitness and highlight the importance of improving fitness for diabetes prevention.

Sleep duration is associated with BMI and waist circumference. However, less is known about whether sleep duration affects different measurements of obesity differently.

To investigate the association between sleep duration and different measures of obesity.

In this cross-sectional analysis 1309, Danish, older adults (55% men) completed at least 3 days of wearing a combined accelerometer and heart rate-monitor for assessing sleep duration (hours/night) within self-reported usual bedtime. Participants underwent anthropometry and ultrasonography to assess BMI, waist circumference, visceral fat, subcutaneous fat, and fat percentage. Linear regression analyses examined the associations between sleep duration and obesity-related outcomes.

Sleep duration was inversely associated with all obesity-related outcomes, except visceral-/subcutaneous-fat-ratio. After multivariate adjustment the magnitude of associations became stronger and statistically significant for all outcomes except visceral-/subcutaneous-fat-ratio, and subcutaneous fat in women. The associations with BMI and waist circumference demonstrated the strongest associations, when comparing standardized regression coefficients.

Shorter sleep duration were associated with higher obesity across all outcomes except visceral-/subcutaneous-fat-ratio. No specifically salient associations with local or central obesity were observed. Results suggest that poor sleep duration and obesity correlate, however, further research is needed to conclude on beneficial effects of sleep duration regarding health and weight loss.

The affiliative relationship between humans and dogs is compared to a mother-infant attachment relationship. We hypothesized that dog's attachment behavior in negative emotional state aroused the owner's attention toward the dog, that is, reduced parasympathetic activity. We measured heart rate variability in both dogs and humans during the Strange Situation Test to examine whether the owners' parasympathetic activity was decreased by being gazed at by their dogs. Our results in a short-term of 6 s before and after the moment the dog gazed at the human face indicated that dogs' parasympathetic activity was lower when the dogs were gazing at their owners than when it was gazing at unfamiliar persons. Dog's autonomic activity was also lower when the dogs were living with their owners for a longer period. However, we could not determine whether gaze from the dog affected the autonomic activity in humans as attachment behavior.

Few large studies have evaluated the relationship between resting heart rate (RHR) and cardiorespiratory fitness. Here we examine cross-sectional and longitudinal relationships between RHR and fitness, explore factors that influence these relationships, and demonstrate the utility of RHR for remote population monitoring.

In cross-sectional analyses (The UK Fenland Study: 5,722 women, 5,143 men, aged 29-65y), we measured RHR (beats per min, bpm) while seated, supine, and during sleep. Fitness was estimated as maximal oxygen consumption (ml⋅min-1⋅kg-1) from an exercise test. Associations between RHR and fitness were evaluated while adjusting for age, sex, adiposity, and physical activity. In longitudinal analyses (6,589 participant subsample), we re-assessed RHR and fitness after a median of 6 years and evaluated the association between within-person change in RHR and fitness. During the coronavirus disease-2019 pandemic, we used a smartphone application to remotely and serially measure RHR (1,914 participant subsample, August 2020 to April 2021) and examined differences in RHR dynamics by pre-pandemic fitness level.

Mean RHR while seated, supine, and during sleep was 67, 64, and 57 bpm. Age-adjusted associations (beta coefficients) between RHR and fitness were -0.26, -0.29, and -0.21 ml⋅kg-1⋅beat-1 in women and -0.27, -0.31, and -0.19 ml⋅kg-1⋅beat-1 in men. Adjustment for adiposity and physical activity attenuated the RHR-to-fitness relationship by 10% and 50%, respectively. Longitudinally, a 1-bpm increase in supine RHR was associated with a 0.23 ml⋅min-1⋅kg-1 decrease in fitness. During the pandemic, RHR increased in those with low pre-pandemic fitness but was stable in others.

RHR is a valid population-level biomarker of cardiorespiratory fitness. Physical activity and adiposity attenuate the relationship between RHR and fitness.

To determine the effects of dietary sugar or carbohydrate restriction on physical activity energy expenditure, energy intake, and physiological outcomes across 24 h.

In a randomized, open-label crossover design, twenty-five healthy men (n = 10) and women (n = 15) consumed three diets over a 24-h period: moderate carbohydrate and sugar content (MODSUG = 50% carbohydrate [20% sugars], 15% protein, 35% fat); low sugar content (LOWSUG = 50% carbohydrate [< 5% sugars], 15% protein, 35% fat); and low carbohydrate content (LOWCHO = 8% carbohydrate [< 5% sugars], 15% protein, 77% fat). Postprandial metabolic responses to a prescribed breakfast (20% EI) were monitored under laboratory conditions before an ad libitum test lunch, with subsequent diet and physical activity monitoring under free-living conditions until blood sample collection the following morning.

The MODSUG, LOWSUG and LOWCHO diets resulted in similar mean [95%CI] rates of both physical activity energy expenditure (771 [624, 919] vs. 677 [565, 789] vs. 802 [614, 991] kcal·d-1; p = 0.29] and energy intake (2071 [1794, 2347] vs. 2195 [1918, 2473] vs. 2194 [1890, 2498] kcal·d-1; P = 0.34), respectively. The LOWCHO condition elicited the lowest glycaemic and insulinaemic responses to breakfast (P < 0.01) but the highest 24-h increase in LDL-cholesterol concentrations (P < 0.001), with no differences between the MODSUG and LOWSUG treatments. Leptin concentrations decreased over 24-h of consuming LOWCHO relative to LOWSUG (p < 0.01).

When energy density is controlled for, restricting either sugar or total dietary carbohydrate does not modulate physical activity level or energy intake over a 24-h period (~ 19-h free-living) despite substantial metabolic changes.

NCT03509610, https://clinicaltrials.gov/show/NCT03509610.

Cardiorespiratory fitness is an established predictor of metabolic disease and mortality. Fitness is directly measured as maximal oxygen consumption (VO2max), or indirectly assessed using heart rate responses to standard exercise tests. However, such testing is costly and burdensome because it requires specialized equipment such as treadmills and oxygen masks, limiting its utility. Modern wearables capture dynamic real-world data which could improve fitness prediction. In this work, we design algorithms and models that convert raw wearable sensor data into cardiorespiratory fitness estimates. We validate these estimates' ability to capture fitness profiles in free-living conditions using the Fenland Study (N=11,059), along with its longitudinal cohort (N = 2675), and a third external cohort using the UK Biobank Validation Study (N = 181) who underwent maximal VO2max testing, the gold standard measurement of fitness. Our results show that the combination of wearables and other biomarkers as inputs to neural networks yields a strong correlation to ground truth in a holdout sample (r = 0.82, 95CI 0.80-0.83), outperforming other approaches and models and detects fitness change over time (e.g., after 7 years). We also show how the model's latent space can be used for fitness-aware patient subtyping paving the way to scalable interventions and personalized trial recruitment. These results demonstrate the value of wearables for fitness estimation that today can be measured only with laboratory tests.

Anthropogenic climate change has caused extreme temperatures worldwide, with data showing that sub-Saharan Africa is especially vulnerable to these changes. In sub-Saharan Africa, women comprise 50% of the agricultural workforce, often working throughout pregnancy despite heat exposure increasing the risk of adverse birth outcomes. In this study, we aimed to improve understanding of the pathophysiological mechanisms responsible for the adverse health outcomes resulting from environmental heat stress in pregnant subsistence farmers. We also aimed to provide data to establish whether environmental heat stress also has physiological effects on the fetus.

We conducted an observational cohort study in West Kiang, The Gambia, at the field station for the Medical Research Council Unit The Gambia at London School of Hygiene & Tropical Medicine (named the MRC Keneba field station). Pregnant women who were aged 16 years or older and who were at <36 weeks' gestation of any gravida or parity were invited to participate in the study. Participants were eligible if they were involved in agricultural or related manual daily tasks of living. Participants were ineligible if they refused to provide consent, had multiple pregnancies (eg, if they had twins), were acutely unwell, or were diagnosed with pre-eclampsia or eclampsia. Heat stress was measured by wet bulb globe temperature (WBGT) and by using the universal thermal climate index (UTCI), and maternal heat strain was directly measured by modified physiological strain index calculated from heart rate and skin temperature. Outcome measures of fetal heart rate (FHR) and fetal strain (defined as a FHR >160 beats per min [bpm] or <115 bpm, or increase in umbilical artery resistance index) were measured at rest and during the working period. Multivariable repeated measure models (linear regression for FHR, and logistic regression for fetal strain) were used to evaluate the association of heat stress and heat strain with acute fetal strain.

Between Aug 26, 2019, and March 27, 2020, 92 eligible participants were recruited to the study. Extreme heat exposure was frequent, with average exposures of WBGT of 27·2°C (SD 3·6°C) and UTCI equivalent temperature of 34·0°C (SD 3·7°C). The total effect of UTCI on fetal strain resulted in an odds ratio (OR) of 1·17 (95% CI 1·09-1·29; p<0·0001), with an adjusted direct effect of OR of 1·12 (1·03-1·21; p=0·010) with each 1°C increase in UTCI. The adjusted OR of maternal heat strain on fetal strain was 1·20 (1·01-1·43; p=0·038), using the UTCI model, with each unit increase.

Data from our study show that decreasing maternal exposure to heat stress and heat strain is likely to reduce fetal strain, with the potential to reduce adverse birth outcomes. Further work that explores the association between heat stress and pregnancy outcomes in a variety of settings and populations is urgently needed to develop effective interventions.

The Wellcome Trust.

Research on the associations of physical activity and cardiorespiratory fitness with β-cell dysfunction and insulin resistance among adults in Sub-Saharan Africa (SSA) is limited. We assessed the association of physical activity and cardiorespiratory fitness with β-cell function, insulin resistance and diabetes among people living with HIV (PLWH) ART-naïve and HIV-uninfected Tanzanian adults.

In a cross-sectional study, we collected data on socio-demography, anthropometry, fat mass and fat free mass and C-reactive protein. Data on glucose and insulin collected during an oral glucose tolerance test were used to assess β-cell dysfunction (defined as insulinogenic index <0.71 (mU/L)/(mmol/L), HOMA-β index <38.3 (mU/L)/(mmol/L), and overall insulin release index <33.3 (mU/L)/(mmol/L)), oral disposition index <0.16 (mU/L)/(mg/dL)(mU/L)-1, insulin resistance (HOMA-IR index >1.9 (mU/L)/(mmol/L) and Matsuda index <7.2 (mU/L)/(mmol/L), prediabetes and diabetes which were the dependent variables. Physical activity energy expenditure (PAEE), sleeping heart rate (SHR), and maximum uptake of oxygen during exercise (VO2 max) were the independent variables and were assessed using a combined heart rate and accelerometer monitor. Logistic regressions were used to assess the associations.

Of 391 participants, 272 were PLWH and 119 HIV-uninfected. The mean age was 39 ( ± 10.5) years and 60% (n=235) were females. Compared to lower tertile, middle tertile of PAEE was associated with lower odds of abnormal insulinogenic index (OR=0.48, 95%CI: 0.27, 0.82). A 5 kj/kg/day increment of PAEE was associated with lower odds of abnormal HOMA-IR (OR=0.91, 95%CI: 0.84, 0.98), and reduced risk of pre-diabetes (RRR=0.98, 95%CI: 0.96, 0.99) and diabetes (RRR=0.92, 95%CI: 0.88, 0.96). An increment of 5 beats per min of SHR was associated with higher risk of diabetes (RRR=1.06, 95%CI: 1.01, 1.11). An increase of 5 mLO2/kg/min of VO2 max was associated with lower risk of pre-diabetes (RRR=0.91, 95%CI: 0.86, 0.97), but not diabetes. HIV status did not modify any of these associations (interaction, p>0.05).

Among Tanzanian adults PLWH and HIV-uninfected individuals, low physical activity was associated with β-cell dysfunction, insulin resistance and diabetes. Research is needed to assess if physical activity interventions can improve β-cell function and insulin sensitivity to reduce risk of diabetes and delay progression of diabetes in SSA.

This cross-sectional study of older adults ≥ 65 years describes daily and hourly patterns of accelerometer-derived steps, sedentary, and physical activity behaviors and examines differences by day of the week and sociodemographic and health-related factors to identify time-use patterns. Data were from 459 Atherosclerosis Risk in Communities (ARIC) study participants (60% female; mean ± SD age = 78.3 ± 4.6 years; 20% Black) who wore a hip accelerometer ≥ 4 of 7 days, for ≥ 10 h/day in 2016. We used linear mixed models to examine daily patterns of steps, sedentary, low light, high light, and moderate-to-vigorous intensity physical activity (MVPA). Differences by sex, median age ( Collapse

We investigated the associations of physical activity (PA), sedentary time (ST), and diet quality with biomarkers of inflammation in 390 children (192 girls, 198 boys) aged 6-8 years. PA energy expenditure (PAEE), light PA, moderate PA (MPA), vigorous PA (VPA), moderate-to-vigorous PA (MVPA), and ST were assessed by combined movement and heart rate sensor. Finnish Children Healthy Eating Index was calculated using data from 4 d food records. Body fat percentage (BF%) was measured by dual-energy X-ray absorptiometry. High-sensitivity C-reactive protein (Hs-CRP), leptin, interleukin-6 (IL-6), adiponectin, tumour necrosis factor-α, and glycoprotein acetyls were measured from fasting blood samples. PAEE, MPA, VPA, and MVPA were inversely associated with hs-CRP (β=-191 to -139, 95% CI=-0.294 to -0.024), leptin (β=-0.409 to -0.301, 95% CI=-0.499 to -0.107), IL-6 (β=-0.136 to -0.104, 95% CI=-0.240 to -0.001) and PAEE, MPA, and MVPA were inversely associated with glycoprotein acetyls (β=-0.117 to -0.103, 95% CI=-0.213 to -0.001). ST was directly associated with hs-CRP (β=0.170, 95% CI=0.070-0.269), leptin (β=0.355, 95% CI=0.265-0.445), and IL-6 (β=0.105, 95% CI=0.005-0.205). VPA was inversely associated with hs-CRP, leptin, and IL-6 in children with higher BF% (β=-0.344 to -0.181, 95% CI=-0.477 to -0.033) but not among children with lower BF% (β=-0.007-0.033, 95% CI=-0.183-0.184). In conclusion, PA was inversely and ST directly associated with circulating levels of biomarkers of inflammation among children. Furthermore, we observed that PA was inversely associated with these biomarkers for inflammation in children with a higher BF%. HighlightsSystemic inflammation, as indicated by increased circulating concentrations of biomarkers for inflammation, may be important in causal pathways leading to insulin resistance, sub-clinical atherosclerosis, and eventually clinical manifestations of cardiovascular diseases.Higher levels of physical activity and lower levels of sedentary time were associated with more favourable inflammatory profile.Body fat percentage modified these associations and especially vigorous intensity physical activity was inversely associated with biomarkers of inflammation on children with higher body fat percentage but not in children with lower body fat percentage.

Although it is widely recognized that physical activity is an important determinant of health, assessing this complex behavior is a considerable challenge.

The purpose of this systematic review and meta-analysis is to examine, quantify, and report the current state of evidence for the validity of energy expenditure, heart rate, and steps measured by recent combined-sensing Fitbits.

We conducted a systematic review and Bland-Altman meta-analysis of validation studies of combined-sensing Fitbits against reference measures of energy expenditure, heart rate, and steps.

A total of 52 studies were included in the systematic review. Among the 52 studies, 41 (79%) were included in the meta-analysis, representing 203 individual comparisons between Fitbit devices and a criterion measure (ie, n=117, 57.6% for heart rate; n=49, 24.1% for energy expenditure; and n=37, 18.2% for steps). Overall, most authors of the included studies concluded that recent Fitbit models underestimate heart rate, energy expenditure, and steps compared with criterion measures. These independent conclusions aligned with the results of the pooled meta-analyses showing an average underestimation of -2.99 beats per minute (k comparison=74), -2.77 kcal per minute (k comparison=29), and -3.11 steps per minute (k comparison=19), respectively, of the Fitbit compared with the criterion measure (results obtained after removing the high risk of bias studies; population limit of agreements for heart rate, energy expenditure, and steps: -23.99 to 18.01, -12.75 to 7.41, and -13.07 to 6.86, respectively).

Fitbit devices are likely to underestimate heart rate, energy expenditure, and steps. The estimation of these measurements varied by the quality of the study, age of the participants, type of activities, and the model of Fitbit. The qualitative conclusions of most studies aligned with the results of the meta-analysis. Although the expected level of accuracy might vary from one context to another, this underestimation can be acceptable, on average, for steps and heart rate. However, the measurement of energy expenditure may be inaccurate for some research purposes.

The agro-pastoralist Maasai of East Africa are highly physically active, but their aerobic fitness has so far only been estimated using heart rate (HR) response to submaximal exercise and not directly measured. Thus, we aimed to measure aerobic fitness directly using respiratory gas analysis in a group of Maasai, and habitual physical activity energy expenditure (PAEE) as explanatory variable.

In total, 21 (10 rural, 11 semi-urban) of 30 volunteering Tanzanian Maasai men were eligible to participate. Respiratory gas exchange was measured during a graded exercise test until exhaustion on a stationary bicycle to determine aerobic fitness. Maximal effort criteria were at least two of the following (1) leveling off, (2) respiratory exchange ratio (RER) >1.10, and (3) maximum HR within 10 bpm of age-estimated maximum HR. Habitual PAEE was estimated using combined accelerometry and HR monitoring. Anthropometry, biochemistry, blood pressure, resting HR, and dietary intake information were collected for background information.

Mean age was 43.2 (range 26-60) years, and hemoglobin was higher in the rural versus semi-urban Maasai (16.9 vs. 15.4 g/dl, p = .02). Mean aerobic fitness (34.4 vs. 33.3 mlO2 /min/kg, p = .79), and mean PAEE (58.5 vs. 52.9 kJ/day/kg, p = .64) were similar in rural and semi-urban Maasai, respectively.

Aerobic fitness was low to moderate in male rural and semi-urban Maasai. This may be explained by relatively low PAEE in comparison to previous objectively measured activity levels in Maasai, which indicates recent lifestyle changes.

In the HIV-infected individuals, physical activity improves physical strength, quality of life and reduces the risk of developing non-communicable diseases. In Sub-Saharan Africa, HIV-infected patients report being less active compared to HIV-uninfected individuals. We assessed the levels and correlates of objectively measured physical activity and capacity among HIV-infected antiretroviral therapy (ART)-naive individuals compared to HIV-uninfected individuals in Mwanza, Tanzania.

We conducted a cross-sectional study among newly diagnosed HIV-infected ART-naive individuals and HIV-uninfected individuals frequency-matched for age and sex. Socio-demographic data, anthropometrics, CD4 counts, haemoglobin level, and C-reactive protein (CRP) were collected. Physical activity energy expenditure (PAEE) was assessed as measure of physical activity whereas sleeping heart rate (SHR) and grip strength were assessed as measures of physical capacity. Multivariable linear regression was used to assess the correlates associated with physical activity and capacity.

A total of 272 HIV-infected and 119 HIV-uninfected individuals, mean age 39 years and 60% women participated in the study. Compared to HIV-uninfected individuals, HIV-infected had poorer physical activity and capacity: lower PAEE (-7.3 kj/kg/day, 95% CI: -11.2, -3.3), elevated SHR (7.7 beats/min, 95%CI: 10.1, 5.3) and reduced grip strength (-4.7 kg, 95%CI: -6.8, -2.8). In HIV-infected individuals, low body mass index, moderate-severe anaemia, low CD4 counts and high CRP were associated with lower physical activity and capacity. In HIV-uninfected individuals, abdominal obesity and moderate anaemia were associated with lower physical activity and capacity.

HIV-infected participants had lower levels of physical activity and capacity than HIV-uninfected participants. Correlates of physical activity and capacity differed by HIV status. Management of HIV and related conditions needs to be provided effectively in health care facilities. Interventions promoting physical activity in these populations will be of importance to improve their health and reduce the risk of non-communicable diseases.

This study aimed to examine (1) adherence to 24 h movement guidelines over a 2 years follow-up in children aged 6-8 years and (2) association of this adherence with cardiometabolic risk factors. Physical activity and sleep were assessed by a monitor combining heart rate and accelerometry measurements. Screen time was reported by the parents. Body fat percentage, waist circumference, blood glucose, serum insulin, plasma lipids, and blood pressure were assessed, and a cardiometabolic risk score was calculated using z-scores. Children were classified as meeting the guidelines if they had on average ≥60 min/day of moderate-to-vigorous physical activity during the valid days; ≤120 min/day of screen time; and 9-11 h/day of sleep. In total, 485 children had valid data at baseline or at 2 years follow-up. Analyses were conducted using adjusted logistic and linear regression models. Most children adhered to the 24 h movement guidelines at baseline, but the adherence decreased over the 2 years follow-up. Meeting physical activity guidelines individually, or in combination with screen time and/or sleep, was longitudinally associated with a lower cardiometabolic risk score, insulin and waist circumference, and cross-sectionally additionally with lower diastolic blood pressure and higher high-density lipoprotein cholesterol. However, these associations became statistically non-significant after adjustment for body fat. In conclusion, meeting 24 h movement guidelines at baseline increases the odds of meeting them at 2 years follow-up in school-aged children. Furthermore, meeting 24 h movement guidelines is associated with lower levels of cardiometabolic risk factors, but these associations are partly explained by lower body fat. Thus, promoting movement behaviors, especially physical activity, and healthy weight in early childhood is important in supporting cardiometabolic health in children.

Physical activity energy expenditure (PAEE) represents the total volume of all physical activity. This can be accumulated as different underlying intensity profiles. Although volume and intensity have been studied in isolation, less is known about their joint association with health. We examined this association with body fatness in a population-based sample of middle-aged British adults.

In total, 6148 women and 5320 men from the Fenland study with objectively measured physical activity from individually calibrated combined heart rate and movement sensing and DXA-derived body fat percentage (BF%) were included in the analyses. We used linear and compositional isocaloric substitution analysis to examine associations of PAEE and its intensity composition with body fatness. Sex-stratified models were adjusted for socio-economic and dietary covariates.

PAEE was inversely associated with body fatness in women (beta = -0.16 (95% CI: -0.17; -0.15) BF% per kJ day-1 kg-1) and men (beta = -0.09 (95% CI: -0.10; -0.08) BF% per kJ day-1 kg-1). Intensity composition was significantly associated with body fatness, beyond that of PAEE; the reallocation of energy to vigorous physical activity (>6 METs) from other intensities was associated with less body fatness, whereas light activity (1.5-3 METs) was positively associated. However, light activity was the main driver of overall PAEE volume, and the relative importance of intensity was marginal compared to that of volume; the difference between PAEE in tertile 1 and 2 in women was associated with 3 percentage-point lower BF%. Higher vigorous physical activity in the same group to the maximum observed value was associated with 1 percentage-point lower BF%.

In this large, population-based cohort study with objective measures, PAEE was inversely associated with body fatness. Beyond the PAEE association, greater levels of intense activity were also associated with lower body fatness. This contribution was marginal relative to PAEE. These findings support current guidelines for physical activity which emphasise that any movement is beneficial, rather than specific activity intensity or duration targets.

It remains unclear to what extent habitual physical activity and sedentary time (ST) are associated with visceral fat and liver fat. We studied the substitution of ST with time spent physically active and total body fat (TBF), visceral adipose tissue (VAT), and hepatic triglyceride content (HTGC) in middle-age men and women.

In this cross-sectional analysis of the Netherlands Epidemiology of Obesity study, physical activity was assessed in 228 participants using a combined accelerometer and heart rate monitor. TBF was assessed by the Tanita bioelectrical impedance, VAT by magnetic resonance imaging, and HTGC by proton-MR spectroscopy. Behavioral intensity distribution was categorized as ST, time spent in light physical activity (LPA), and moderate to vigorous physical activity (MVPA). To estimate the effect of replacing 30 min·d-1 of ST with 30 min·d-1 LPA or MVPA, we performed isotemporal substitution analyses, adjusted for sex, age, ethnicity, education, the Dutch Healthy Diet index, and smoking.

Included participants (41% men) had a mean ± SD age of 56 ± 6 yr and spent 88 ± 56 min in MVPA and 9.0 ± 2.1 h of ST. Replacing 30 min·d-1 of ST with 30 min of MVPA was associated with 1.3% less TBF (95% confidence interval = -2.0 to -0.7), 7.8 cm2 less VAT (-11.6 to -4.0), and 0.89 times HTGC (0.82-0.97). Replacement with LPA was not associated with TBF (-0.03%; -0.5 to 0.4), VAT (-1.7 cm2; -4.4 to 0.9), or HTGC (0.98 times; 0.92-1.04).

Reallocation of time spent sedentary with time spent in MVPA, but not LPA, was associated with less TBF, visceral fat, and liver fat. These findings contribute to the development of more specified guidelines on ST and physical activity.

Objectives.To investigate the validity of different devices and algorithms used in military organizations worldwide to assess physical activity energy expenditure (PAEE) and heart rate (HR) among soldiers.Design.Device validation study.Methods. Twenty-three male participants serving their mandatory military service accomplished, firstly, nine different military specific activities indoors, and secondly, a normal military routine outdoors. Participants wore simultaneously an ActiHeart, Everion, MetaMax 3B, Garmin Fenix 3, Hidalgo EQ02, and PADIS 2.0 system. The PAEE and HR data of each system were compared to the criterion measures MetaMax 3B and Hidalgo EQ02, respectively.Results. Overall, the recorded systematic errors in PAEE estimation ranged from 0.1 (±1.8) kcal.min^-1to -1.7 (±1.8) kcal.min^-1for the systems PADIS 2.0 and Hidalgo EQ02 running the Royal Dutch Army algorithm, respectively, and in the HR assessment ranged from -0.1 (±2.1) b.min^-1to 0.8 (±3.0) b.min^-1for the PADIS 2.0 and ActiHeart systems, respectively. The mean absolute percentage error (MAPE) in PAEE estimation ranged from 29.9% to 75.1%, with only the Everion system showing an overall MAPE <30%, but all investigated devices reported overall MAPE <1.4% in the HR assessment.Conclusions. The present study demonstrated poor to moderate validity in terms of PAEE estimation, but excellent validity in all investigated devices in terms of HR assessment. Overall, the Everion performed among the best in both parameters and with a device placement on the upper arm, the Everion system is particularly useful during military service, as it does not interfere with other relevant equipment.

The association between the International Physical Activity Questionnaire Short Form (IPAQ-SF) and objective measures of physical activity has never been evaluated in participants with newly diagnosed bipolar disorder (BD). Our aim was to compare IPAQ-SF to objective measures in participants with newly diagnosed BD, their unaffected first-degree relatives (UR), and healthy control individuals (HC) in groups combined and stratified by group.

Physical activity measurements were collected on 20 participants with newly diagnosed BD, 20 of their UR, and 20 HC using individually calibrated combined acceleration and heart rate sensing (Actiheart) for seven days. IPAQ-SF was self-completed at baseline. Correlation between measurements from the two methods was examined with Spearman rank correlation coefficient and agreement levels examined with modified Bland-Altman plots.

Physical activity energy expenditure (PAEE) from IPAQ-SF was weakly but significantly positively correlated with physical activity estimates measured using acceleration and heart rate in groups combined (Actiheart PAEE) (ρ= 0.301, p = 0.02). Correlations for each group were positive, but only in UR were it statistically significant (BD: p = 0.18, UR: p = 0.007, HC: p = 0.84). Self-reported PAEE and moderate-intensity were markedly underestimated [PAEE in all participants combined: 62.7 (Actiheart) vs. 24.3 kJ/day/kg (IPAQ-SF), p < 0.001], while vigorous-intensity was overestimated. Bland-Altman plots indicated proportional bias.

These results suggest that the use of the IPAQ-SF to monitor levels of physical activity in participants with newly diagnosed BD, in a psychiatric clinical setting, should be used with caution and consideration.

Low energy availability (EA) suppresses many physiological processes, including ovarian function in female athletes. Low EA could also predispose athletes to develop a state of overreaching. This study compared the changes in ad libitum energy intake (EI), exercise energy expenditure (ExEE), and EA among runners completing a training overload (TO) phase. We tested the hypothesis that runners becoming overreached would show decreased EA, suppressed ovarian function and plasma leptin, compared with well-adapted (WA) runners. After 1 menstrual cycle (baseline), 16 eumenorrheic runners performed 4 weeks of TO followed by a 2-week recovery (131 ± 3% and 63 ± 6% of baseline running volume, respectively). Seven-day ExEE, EI, running performance (RUN_perf) and plasma leptin concentration were assessed for each phase. Salivary estradiol concentration was measured daily. Urinary luteinizing hormone concentration tests confirmed ovulation. Nine runners adapted positively to TO (WA, ΔRUN_perf: +4 ± 2%); 7 were non-functionally overreached (NFOR; ΔRUN_perf: -9 ± 2%) as RUN_perf remained suppressed after the recovery period. WA increased EI during TO, maintaining their baseline EA despite a large increase in ExEE (ΔEA = +1.9 ± 1.3 kcal·kg fat free mass (FFM)^-1·d^-1, P = 0.17). By contrast, NFOR showed no change in EI, leading to decreased EA (ΔEA = -5.6 ± 2.1 kcal·kg FFM^-1·d^-1, P = 0.04). Plasma leptin concentration mid-cycle and luteal salivary estradiol concentration decreased in NFOR only. Contrasting with WA, NFOR failed to maintain baseline EA during TO, resulting in poor performance outcomes and suppressed ovarian function. ClinicalTrials.gov no. NCT02224976. Novelty: Runners adapting positively to training overload (TO) increased ad libitum energy intake, maintaining baseline EA and ovarian function through TO. By contrast, NFOR runners failed to increase energy intake, showing suppressed EA and ovarian function during TO.