Background Physical activity and sedentary behaviour have been suggested to independently affect a number of health outcomes. To what extent different combinations of physical activity and sedentary behaviour may influence physical function and frailty outcomes in older adults is unknown. The aim of this study was to examine the combination of mutually exclusive categories of accelerometer-measured physical activity and sedentary time on physical function and frailty in older adults. Methods 771 older adults (54% women; 76.8 ± 4.9 years) from the Toledo Study for Healthy Aging participated in this cross-sectional study. Physical activity and sedentary time were measured by accelerometry. Physically active was defined as meeting current aerobic guidelines for older adults proposed by the World Health Organization. Low sedentary was defined as residing in the lowest quartile of the light physical activity-to-sedentary time ratio. Participants were then classified into one of four mutually exclusive movement patterns: (1) ‘physically active & low sedentary’, (2) ‘physically active & high sedentary’, (3) ‘physically inactive & low sedentary’, and (4) ‘physically inactive & high sedentary’. The Short Physical Performance Battery was used to measure physical function and frailty was assessed using the Frailty Trait Scale. Results ‘Physically active & low sedentary’ and ‘physically active & high sedentary’ individuals had significantly higher levels of physical function (β = 1.73 and β = 1.30 respectively; all p < 0.001) and lower frailty (β = − 13.96 and β = − 8.71 respectively; all p < 0.001) compared to ‘physically inactive & high sedentary’ participants. Likewise, ‘physically inactive & low sedentary’ group had significantly lower frailty (β = − 2.50; p = 0.05), but significance was not reached for physical function. Conclusions We found a dose-response association of the different movement patterns analysed in this study with physical function and frailty. Meeting the physical activity guidelines was associated with the most beneficial physical function and frailty profiles in our sample. Among inactive people, more light intensity relative to sedentary time was associated with better frailty status. These results point out to the possibility of stepwise interventions (i.e. targeting less strenuous activities) to promote successful aging, particularly in inactive older adults.

Accelerometers’ accuracy for sedentary time (ST) and moderate-to-vigorous physical activity (MVPA) classification depends on accelerometer placement, data processing, activities, and sample characteristics. As intensities differ by age, this study sought to determine intensity cut-points at various wear locations people more than 70 years old. Data from 59 older adults were used for calibration and from 21 independent participants for cross-validation purposes. Participants wore accelerometers on their hip and wrists while performing activities and having their energy expenditure measured with portable calorimetry. ST and MVPA were defined as ≤1.5 metabolic equivalents (METs) and ≥3 METs (1 MET = 2.8 mL/kg/min), respectively. Receiver operator characteristic (ROC) analyses showed fair-to-good accuracy (area under the curve [AUC] = 0.62–0.89). ST cut-points were 7 mg (cross-validation: sensitivity = 0.88, specificity = 0.80) and 1 count/5 s (cross-validation: sensitivity = 0.91, specificity = 0.96) for the hip; 18 mg (cross-validation: sensitivity = 0.86, specificity = 0.86) and 102 counts/5 s (cross-validation: sensitivity = 0.91, specificity = 0.92) for the non-dominant wrist; and 22 mg and 175 counts/5 s (not cross-validated) for the dominant wrist. MVPA cut-points were 14 mg (cross-validation: sensitivity = 0.70, specificity = 0.99) and 54 count/5 s (cross-validation: sensitivity = 1.00, specificity = 0.96) for the hip; 60 mg (cross-validation: sensitivity = 0.83, specificity = 0.99) and 182 counts/5 s (cross-validation: sensitivity = 1.00, specificity = 0.89) for the non-dominant wrist; and 64 mg and 268 counts/5 s (not cross-validated) for the dominant wrist. These cut-points can classify ST and MVPA in older adults from hip- and wrist-worn accelerometers.

Background It is important for sport scientists and health professionals to have estimative methods for energy demand during different physical activities. The metabolic equivalent of task (MET) provides a feasible approach for classifying activity intensity as a multiple of the resting metabolic rate (RMR). RMR is generally assumed to be 3.5 mL of oxygen per kilogram of body mass per minute (mL O 2 kg −1 min −1 ), a value that has been criticized and considered to be overestimated in the older adult population. However, there has been no comprehensive effort to review available RMR estimations, equivalent to 1 MET, obtained in the older adult population. Objective The aim of this review was to examine the existing evidence reporting measured RMR values in the older adult population and to provide descriptive estimates of 1 MET. Methods A systematic review was conducted by searching PubMed, Web of Science, Scopus, CINAHL, SPORTDiscus, and Cochrane Library, from database inception to July 2021. To this end, original research studies assessing RMR in adults ≥ 60 years old using indirect calorimetry and reporting results in mL O 2 kg −1 min −1 were sought. Results Twenty-three eligible studies were identified, including a total of 1091 participants (426 men). All but two studies reported RMR values lower than the conventional 3.5 mL O 2 kg −1 min −1 . The overall weighted average 1 MET value obtained from all included studies was 2.7 ± 0.6 mL O 2 kg −1 min −1 ; however, when considering best practice studies, this value was 11% lower (2.4 ± 0.3 mL O 2 kg −1 min −1 ). Conclusion Based on the results of this systematic review, we would advise against the application of the standard value of 1 MET (3.5 mL O 2 kg −1 min −1 ) in people ≥ 60 years of age and encourage the direct assessment of RMR using indirect calorimetry while adhering to evidence-based best practice recommendations. When this is not possible, assuming an overall value of 2.7 mL O 2 kg −1 min −1 might be reasonable. Systematic review registration: International Prospective Register of Systematic Reviews on 30 September 2020, with registration number CRD42020206440.Asier Mañas and Ignacio Ara equally contributed.

This study aimed to compare the Cosmed K5 portable indirect calorimeter, using the mixing chamber mode and face mask, with a stationary metabolic cart when measuring the resting metabolic rate (RMR) and to derive fitting equations if discrepancies are observed. Forty‐three adults (18–84 years) were assessed for their RMR for two 30‐min consecutive and counterbalanced periods using a Cosmed K5 and an Oxycon Pro. Differences among devices were tested using paired sample Student's t‐tests, and correlation and agreement were assessed using Pearson's correlation coefficients, intraclass correlation coefficient and Bland–Altman plots. Forward stepwise multiple linear regression models were performed to develop fitting equations for estimating differences among devices when assessing oxygen uptake (VO2diff, mL·min−1) and carbon dioxide production (VCO2diff, mL·min−1). Furthermore, the Oxycon Pro was tested before being confirmed as a reference device. Significant differences between devices were found in most metabolic and ventilatory parameters, including the primary outcomes of VO2 and VCO2. These differences showed an overestimation of the Cosmed K5 in all metabolic outcomes, except for Fat, when compared to the Oxycon Pro. When derived fitting equations were applied (VO2diff − 139.210 + 0.786 [weight, kg] + 1.761 [height, cm] – 0.941 [Cosmed K5 VO2, mL·min−1]; VCO2diff − 86.569 + 0.548 [weight, kg] + 0.915 [height, cm] – 0.728 [Cosmed K5 VCO2, mL·min−1]), differences were minimized, and agreement was maximized. This study provides fitting equations which allow the use of the Cosmed K5 for reasonably optimal RMR determinations.