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OBJECTIVE To investigate the association between accelerometer-derived physical activity energy expenditure (PAEE) and incident type 2 diabetes (T2D) in a cohort of middle-aged adults and within subgroups. RESEARCH DESIGN AND METHODS Data were from 90,096 UK Biobank participants without prevalent diabetes (mean 62 years of age; 57% women) who wore a wrist accelerometer for 7 days. PAEE was derived from wrist acceleration using a population-specific method validated against doubly labeled water. Logistic regressions were used to assess associations between PAEE, its underlying intensity, and incident T2D, ascertained using hospital episode and mortality data up to November 2020. Models were progressively adjusted for demographic, lifestyle factors, and BMI. RESULTS The association between PAEE and T2D was approximately linear (n = 2,018 events). We observed 19% (95% CI 17–21) lower odds of T2D per 5 kJ · kg−1 · day−1 in PAEE without adjustment for BMI and 11% (9–13) with BMI adjustment. The association was stronger in men than women and weaker in those with obesity and higher genetic susceptibility to obesity. There was no evidence of effect modification by genetic susceptibility to T2D or insulin resistance. For a given level of PAEE, odds of T2D were lower among those engaging in more moderate-to-vigorous activity. CONCLUSIONS There was a strong linear relationship between PAEE and incident T2D. A difference in PAEE equivalent to an additional daily 20-min brisk walk was associated with 19% lower odds of T2D. The association was broadly similar across population subgroups, supporting physical activity for diabetes prevention in the whole population.

BackgroundCardiorespiratory fitness is rarely measured in population studies. Most studies of fitness do not examine differences by population subgroups or seasonal trends.MethodsWe used a validated submaximal exercise test to measure fitness in 5976 women and 5316 men, residing in England. We expressed fitness as maximal oxygen consumption per kilogram total body mass (VO2maxtbm) and fat free mass (VO2maxffm). Descriptive statistics were computed across anthropometric and sociodemographic characteristics, as well as across the year. Progressive multivariable analyses were performed to examine mediation by physical activity energy expenditure (PAEE) and BMI.ResultsMean±SD VO2maxtbm was lower in women (35.4±7.6 ml·min-1·kg-1) than men (42.1±7.4 ml·min-1·kg-1) but VO2maxffm was similar (women: 59.7±11.8 ml·min-1·kg-1; men: 62.5±10.4 ml·min-1·kg-1). Fitness was inversely associated with age but not after adjustment for PAEE. People in more physically demanding jobs were fitter compared to those in sedentary jobs but this association was attenuated in women and reversed in men following adjustment for total PAEE. PAEE and BMI and were positively associated with fitness at all levels of adjustment when fitness was expressed relative to fat-free mass. Fitness during summer was 4% higher than the winter among women, but did not differ by season among men.ConclusionsFitness was inversely associated with age but less steeply than anticipated, suggesting older generations are comparatively fitter than younger generations. PAEE and BMI were stronger determinants of the variance in fitness than any other characteristic including age. This emphasizes the importance of modifiable physical activity behaviours in public health interventions.KEY MESSAGESFitness was inversely associated with age but less steeply than anticipated, suggesting older generations are comparatively fitter than younger generationsRelationships between cardiorespiratory fitness and sociodemographic characteristics were primarily mediated by physical activity.A one standard deviation difference in physical activity had the same impact on cardiorespiratory fitness as being 25 years younger

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

BackgroundCardiorespiratory fitness (CRF) was assessed in UK Biobank (UKB) using heart rate response to a submaximal ramped cycle ergometer test that was individualised for participant characteristics including cardiovascular disease risk. Studies have since explored health associations with CRF by estimating maximal oxygen consumption (VO2max) from UKB test data using interpretation methods that have not accounted for this individualisation procedure. Thus, dose-response relationships reported in these studies may be inaccurate. We developed and validated a novel VO2max estimation approach that accounts for the UKB test individualisation procedure and compared dose-response relationships with health outcomes between the novel and previous methods.MethodsIn a cross-over study (n=189), participants completed several UKB tests and VO2max was measured. A multilevel modelling framework was developed that combines heart rate response features from the UKB test to estimate VO2max. Estimates were compared within participants across UKB test protocols, and with directly measured VO2max. Short-term test-retest reliability was assessed in a subsample of participants (n=87). In UKB, we examined associations between estimated CRF and disease endpoints (n=80,259) and compared associations obtained with an unvalidated method. Long-term test-retest reliability was examined (n = 2877).ResultsEstimated and directly measured VO2max were strongly correlated (Pearson’srrange: 0.68 to 0.74) with no mean bias (women bias: −0.8 to 0.4; men bias range: −0.3 to 0.3), outperforming a previous approach for interpreting UKB test data. Agreement between estimated VO2max across different test protocols was strong (Pearson’srrange: 0.94 to 0.99). Short- and long-term reliability was also high (lambda=0.91 and 0.80, respectively). All-cause mortality was 7% (95%CI 4-10%, 2686 deaths) lower and CVD mortality 9% (95%CI 3-14%, 858 deaths) lower for every 1-MET difference in fitness, associations twice as strong as determined by previous methods.ConclusionsWe present a valid and reliable method for estimating CRF in UKB and demonstrate its utility in characterising dose-response relationships with health outcomes. Accounting for the individualisation procedure strengthens observed relationships between CRF and disease and enhances the case for promoting improved fitness in the general population.

AimsResting heart rate (RHR) is inversely associated with cardiorespiratory fitness (CRF) but few studies have investigated the nature of this relationship in large population samples. We examined the association between RHR and CRF in UK adults and explored factors that may influence this relationship.Methods and ResultsIn a population-based sample of 5,143 men and 5,722 women (aged 29-65 years), mean (SD) RHR while seated, supine, and during sleep was 67.6 (9.8), 63.5 (8.9), and 56.9 (6.9) bpm, respectively. The age- and sex-adjusted association with CRF as assessed by submaximal treadmill testing was −0.26 (95%CI −0.27; −0.24), −0.31 (95%CI −0.33; −0.29), and −0.31 (95%CI −0.34; −0.29) ml O2 kg-1 beat-1. Sequential adjustment for objectively measured obesity and physical activity attenuated the RHR coefficient by 10% and 50%, respectively. In longitudinal analyses of 6,589 participants re-examined after 6 years, each 1 bpm increase in supine RHR was associated with 0.23 (95%CI 0.20; 0.25) ml O2 min-1 kg-1 decrease in CRF.ConclusionsAcross all measures, RHR is inversely associated with CRF; half of this association is explained by obesity and physical activity, suggesting CRF changes achieved through altered behaviour could be tracked through changes in RHR, a notion supported by longitudinal results. As well as its utility as a biomarker of CRF at population-level, serial measurements of RHR may facilitate personal goal setting/evaluation and remote patient monitoring.

Background UK Biobank is a large prospective cohort study containing accelerometer-based physical activity data with strong validity collected from 100,000 participants approximately 5 years after baseline. In contrast, the main cohort has multiple self-reported physical behaviours from > 500,000 participants with longer follow-up time, offering several epidemiological advantages. However, questionnaire methods typically suffer from greater measurement error, and at present there is no tested method for combining these diverse self-reported data to more comprehensively assess the overall dose of physical activity. This study aimed to use the accelerometry sub-cohort to calibrate the self-reported behavioural variables to produce a harmonised estimate of physical activity energy expenditure, and subsequently examine its reliability, validity, and associations with disease outcomes. Methods We calibrated 14 self-reported behavioural variables from the UK Biobank main cohort using the wrist accelerometry sub-cohort (n = 93,425), and used published equations to estimate physical activity energy expenditure (PAEESR). For comparison, we estimated physical activity based on the scoring criteria of the International Physical Activity Questionnaire, and by summing variables for occupational and leisure-time physical activity with no calibration. Test-retest reliability was assessed using data from the UK Biobank repeat assessment (n = 18,905) collected a mean of 4.3 years after baseline. Validity was assessed in an independent validation study (n = 98) with estimates based on doubly labelled water (PAEEDLW). In the main UK Biobank cohort (n = 374,352), Cox regression was used to estimate associations between PAEESR and fatal and non-fatal outcomes including all-cause, cardiovascular diseases, respiratory diseases, and cancers. Results PAEESR explained 27% variance in gold-standard PAEEDLW estimates, with no mean bias. However, error was strongly correlated with PAEEDLW (r = −.98; p SR had narrower range than the criterion. Test-retest reliability (Λ = .67) and relative validity (Spearman = .52) of PAEESR outperformed two common approaches for processing self-report data with no calibration. Predictive validity was demonstrated by associations with morbidity and mortality, e.g. 14% (95%CI: 11–17%) lower mortality for individuals meeting lower physical activity guidelines. Conclusions The PAEESR variable has good reliability and validity for ranking individuals, with no mean bias but correlated error at individual-level. PAEESR outperformed uncalibrated estimates and showed stronger inverse associations with disease outcomes.

The Cambridge Ramped Treadmill Test (CRTT) is an incremental, multistage exercise test (I: steady-state walk, II: walk ramped speed, III: walk ramped incline, and IV: run ramped speed on flat). It is typically deployed as a submaximal test with flexible test termination criteria, making it an attractive option for population-based studies of cardiorespiratory fitness. We conducted a study in healthy adults to test the validity of maximal oxygen consumption estimates (VO2 max; ml O2·kg-1·min-1) predicted from CRTT heart rate response using several methods: a heart rate-to-work rate linear regression method across several test termination criteria, either when a percentage of age-predicted maximal heart rate was achieved (50% through 100%) or at the end of distinct CRTT stages (II, III, and IV); and two single-point walk-test calibration methods using data from either CRTT stage I (low-point method) or stage II (high-point method). For estimates from the linear regression method, prediction bias ranged from −3.0 to −1.6 ml O2·kg-1·min-1 and Pearson’s r ranged from 0.57 to 0.79 for endpoints at percentages of age-predicted maximal heart rate; results were similar for stages III and IV endpoints, but predictions using data only up to stage II had poorer agreement. Agreement was moderate when using the low-point (mean bias: −4.3 ml O2·kg-1·min-1; Pearson’s r: 0.71) and high-point (mean bias: −3.5 ml O2·kg-1·min-1; Pearson’s r: 0.69) methods. Heart rate response to the CRTT can be used to predict VO2max with acceptable validity in common epidemiological scenarios.

OBJECTIVES—: Assessing aging muscle through estimates of muscle heterogeneity may overcome some of the limitations of grayscale analyses. The objectives of this study included determining statistical model parameters that characterize muscle echogenicity and are associated with strength in younger and older participants. METHODS—: Thirty-three community-dwelling participants were assigned to younger and older groups. Quantitative B-mode ultrasound scanning of the rectus femoris and isometric grip strength testing were completed. Shape or dispersion parameters from negative binomial distribution, Nakagami, gamma, and gamma mixture models were fitted to the grayscale histograms. RESULTS—: The mean ages ± SDs of the younger and older groups were 24.0 ± 2.3 and 65.1 ± 6.5 years, respectively. Statistical model shape and dispersion parameters for the grayscale histograms significantly differed between the younger and older participants (P = .002–.006). Among all of the statistical models considered, the gamma mixture model showed the best fit with the grayscale histograms (χ(2) goodness of fit = 62), whereas the Nakagami distribution displayed the poorest fit (χ(2) goodness of fit = 2595). Grayscale values were significantly associated with peak grip strength force in younger adult participants (R(2) = 0.36; P < .008). However, the negative binomial dispersion parameter k (adjusted R(2) = 0.70; P < .001) and gamma shape parameter α (adjusted R(2) = 0.68; P < .01) showed the highest associations with peak grip strength force in older adult participants. CONCLUSIONS—: The negative binomial dispersion parameter k and the gamma shape parameter α have clinical relevance for the assessment of age-related muscle changes. Statistical models of muscle heterogeneity may characterize the association between muscle tissue composition estimates and strength better than grayscale measures in samples of community-dwelling older adults.

Background: This study aimed to test the validity of three different submaximal tests (i.e., 3-min step test with 20.3-cm step box height (3MST20), 3-min step test with 30-cm step box height (3MST30), and 6-min walk test (6MWT)) in estimating maximal oxygen consumption (VO2max) in young and healthy individuals. Methods: The 3MST20, 3MST30, 6MWT, as well as the cardiopulmonary exercise test (CPET) were performed in 73 participants (37 men and 36 women, mean age: 30.8 &plusmn, 9.3 years). All participants visited the clinic three in a random order for anthropometric measurements, three submaximal tests, and the VO2max test. Multiple linear regression analyses were conducted to construct the VO2max prediction equations for each submaximal test. Results: The prediction equations developed based on multiple regression analyses for each submaximal tests were as follows: 3MST20: VO2max = 86.0 &minus, 10.9 &times, sex (male = 1, female = 2) &minus, 0.4 &times, age &minus, 0.1 &times, weight &minus, heart rate recovery at 30 s (HRR30s), 3MST30: VO2max = 84.5 &minus, 10.2 &times, HRR30s, and 6MWT: VO2max = 61.1 &minus, 11.1 &times, 0.2 &times, (distance walked&middot, 10&minus, 1). The estimated VO2max values based on formulated equations were 37.0 &plusmn, 7.9, 37.3 &plusmn, 7.6, and 36.9 &plusmn, 7.9 mL∙kg&minus, 1∙min&minus, 1 derived from the 3MST20, 3MST30, and 6MWT, respectively. These estimated VO2max values were not significantly different from the measured VO2max value, 37.3 mL∙kg&minus, 1. The estimated VO2max based on the 3MST20, 3MST30, and 6MWT results explained 73.4%, 72.2%, and 74.4% of the variances in the measured VO2max (p &lt, 0.001), respectively. Conclusions: The 3MST20, 3MST30, and 6MWT were valid in estimating VO2max in relatively young and healthy Asian individuals.

Aging is associated with impaired upper limb proprioceptive acuity, as reflected by decreased position matching accuracy with increasing task complexity and movement extent. Most studies have primarily used single-joint or planar paradigms to examine age-related changes in proprioception. It is unclear whether these changes can be generalized to more complex multi-joint movements, where additional sensory feedback may affect performance. Since age-related declines in cognitive function may impair the ability to integrate multiple sources of sensory feedback, deficits in position matching ability in older adults may persist when tasks are performed in three-dimensional space. The accuracy with which young and older participants reproduced remembered reference hand positions was assessed under different experimental conditions. Participants matched target locations located directly to the front or 45° to the side relative to the midline using the preferred and non-preferred arms. Either the same (i.e., ipsilateral matching) or the opposite (i.e., contralateral matching) arm was used to reproduce the target location. No differences in matching accuracy were found between young and older participants when matching ipsilaterally. When matching contralaterally, accuracy was worse in older participants for target locations located to the side, which may reflect age-related changes in the perception of peripersonal space. In contrast to previous studies, accuracy did not differ between the preferred and non-preferred arms in either group. These results extend previous findings demonstrating age-related impairments in proprioceptively guided arm movements when interhemispheric transfer is required.

Knee osteoarthritis (OA) is a chronic disease characterized by articular cartilage damage, pain, and weakness. Ambulating stairs is a leading source of pain in people with OA, and disease progression may be marked by asymmetrical gait abnormalities. The purpose of this study was to assess relative peak knee extension torque and Step-Up-and-Over (SUO) test performance asymmetries in individuals with knee OA. Forty-six male Veterans with knee OA (age = 61.6 yrs. ± 5.6) enrolled in the study. Functional performance was assessed via the SUO test, which involves ascending/descending an 8-inch box on a force plate. SUO test outcomes included force-time parameters representing step-up force, step-down force, and force-time curve smoothness. Peak torque was measured using isokinetic dynamometry (180 deg/s). Self-reported symptoms and physical function were evaluated using the Knee injury and Osteoarthritis Outcome Score (KOOS). Force-time curve smoothness was the only SUO test outcome significantly different in the less involved leg (mean = -8.27 ±.55) compared to the more involved leg (mean = -8.48 ±.57, p

The applied use of eccentric muscle actions for physical rehabilitation may utilize the framework of periodization. This approach may facilitate the safe introduction of eccentric exercise and appropriate management of the workload progression. The purpose of this data-driven Hypothesis and Theory paper is to present a periodization model for isokinetic eccentric strengthening of older adults in an outpatient rehabilitation setting. Exemplar and group data are used to describe the initial eccentric exercise prescription, structured familiarization procedures, workload progression algorithm, and feasibility of the exercise regimen. Twenty-four men (61.8 ± 6.3 years of age) completed a 12-week isokinetic eccentric strengthening regimen involving the knee extensors. Feasibility and safety of the regimen was evaluated using serial visual analog scale (VAS, 0–10) values for self-reported pain, and examining changes in the magnitude of mean eccentric power as a function of movement velocity. Motor learning associated with the familiarization sessions was characterized through torque-time curve analysis. Total work was analyzed to identify relative training plateaus or diminished exercise capacity during the progressive phase of the macrocycle. Variability in the mean repetition interval decreased from 68 to 12% during the familiarization phase of the macrocycle. The mean VAS values were 2.9 ± 2.7 at the start of the regimen and 2.6 ± 2.9 following 12 weeks of eccentric strength training. During the progressive phase of the macrocycle, exercise workload increased from 70% of the estimated eccentric peak torque to 141% and total work increased by 185% during this training phase. The slope of the total work performed across the progressive phase of the macrocycle ranged from −5.5 to 29.6, with the lowest slope values occurring during microcycles 8 and 11. Also, mean power generation increased by 25% when eccentric isokinetic velocity increased from 60 to 90° s−1 while maintaining the same workload target. The periodization model used in this study for eccentric exercise familiarization and workload progression was feasible and safe to implement within an outpatient rehabilitation setting. Cyclic implementation of higher eccentric movement velocities, and the addition of active recovery periods, are featured in the proposed theoretical periodization model for isokinetic eccentric strengthening.

Knee osteoarthritis (OA) is a complex condition involving articular cartilage, connective tissue, and skeletal muscle. Diagnostic ultrasound (US) has been proposed as an approach to characterize muscle changes in those with chronic conditions. The purpose of this study was to determine if quantitative US muscle morphology and morphometry measures are associated with clinical markers of knee OA. Male Veterans with knee OA (n=36; age=62.2 ± 5.7 yr; BMI=31.2 ± 6.5) participated in the study. Self-reported symptoms and physical function were evaluated using the Knee injury and Osteoarthritis Outcome Score (KOOS). Knee OA asymmetry was determined by the Kellgren–Lawrence grade and self-reported pain. B-mode quantitative US with a 13–6 MHz linear array transducer was used to obtain tissue echogenicity and muscle thickness values. The primary scanning site was the rectus femoris. Additional sites at the trapezius, deltoid, pectoralis major, and brachioradialis were used as proxy measures of lean body mass (LBM). Lower echogenicity (△ -3.04 grayscale levels, t=2.70, p=.01) and greater muscle thickness (△ .17 cm, t=2.21, p=.03) of the rectus femoris were identified in the less involved limb. Additionally, the summed US muscle thickness values were associated with the Symptom and Sports/Recreation KOOS subscales (r=.37–.39, p=.02–.03), and the deltoid was the only muscle group associated with all 5 KOOS subscales (r=.36–.45, p=.01–.04). In summary, US-derived muscle morphology and morphometry measures identified knee OA asymmetries in Veteran men. Key upper extremity muscle groups and proxy estimates of LBM should not be overlooked as factors that affect the KOOS score.