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Sedentary behaviour (SB) and physical activity (PA) have been shown to be independent modulators of healthy ageing. We thus investigated the impact of activity monitor placement on the accuracy of detecting SB and PA in older adults, as well as a novel random forest algorithm trained on data from older persons. Four monitor types (ActiGraph wGT3X-BT, ActivPAL3c VT, GENEActiv Original, and DynaPort MM+) were simultaneously worn on five anatomical sites during ten different activities by a sample of twenty older adults (70.0 (12.0) years; 10 women). The results indicated that collecting metabolic equivalent (MET) data for 60 s provided the most representative results, minimising variability. In addition, thigh-worn monitors, including ActivPAL, Random Forest, and Sedentary Sphere-Thigh, exhibited superior performance in classifying SB, with balanced accuracies ≥ 94.2%. Other monitors, such as ActiGraph, DynaPort MM+, and GENEActiv Sedentary Sphere-Wrist, demonstrated lower performance. ActivPAL and GENEActiv Random Forest outperformed other monitors in participant-specific balanced accuracies for SB classification. Only thigh-worn monitors achieved acceptable overall balanced accuracies (≥80.0%) for SB, standing, and medium-to-vigorous PA classifications. In conclusion, it is advisable to position accelerometers on the thigh, collect MET data for ≥60 s, and ideally utilise population-specific trained algorithms.

Background: The aim was to determine the reliability and validity of IPAQ measured sedentary behaviour (SB) and moderate-vigorous physical activity (MVPA) in older persons whilst examining any sex differences in reliability and validity results., Method: 89 participants (73.7 ± 6.3 years, 54% female) completed the IPAQ. Participants were fitted with a thigh mounted triaxial accelerometer (GeneActiv Original) for seven consecutive days and subsequently completed a second IPAQ., Results: IPAQ showed weak reliability qualities for Total SB (h·week-1) and 10 minute MVPA (accumulated in bouts ≥ 10 continuous minutes, h·week-1). IPAQ had poor concurrent validity qualities for Total SB, 10 minute MVPA, but not Sporadic MVPA (accumulated in bouts < 10 continuous minutes, h·week-1). IPAQ only categorised participant physical behaviour classification correctly 2% of the time. Sex differences were only present for the correlation slope of IPAQ 10 minute MVPA reliability measures., Conclusion: Our data suggests that the IPAQ is not suitable for assessing older adults habitual physical behaviour.

Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promising future alternative. Hence, the current study compared between cut-off point and machine learning algorithms, for optimal quantification of sedentary behaviour and physical activity intensities in the elderly. Thus, in a heterogeneous sample of forty participants (aged ≥60 years, 50% female) energy expenditure during laboratory-based activities (ranging from sedentary behaviour through to moderate-to-vigorous physical activity) was estimated by indirect calorimetry, whilst wearing triaxial thigh-mounted accelerometers. Three cut-off point algorithms and a Random Forest machine learning model were developed and cross-validated using the collected data. Detailed analyses were performed to check algorithm robustness, and examine and benchmark both overall and participant-specific balanced accuracies. This revealed that the four models can at least be used to confidently monitor sedentary behaviour and moderate-to-vigorous physical activity. Nevertheless, the machine learning algorithm outperformed the cut-off point models by being robust for all individual's physiological and non-physiological characteristics and showing more performance of an acceptable level over the whole range of physical activity intensities. Therefore, we propose that Random Forest machine learning may be optimal for objective assessment of sedentary behaviour and physical activity in older adults using thigh-mounted triaxial accelerometry.

Background: The aim of the study was to provide an isotemporal substitution model to predict how changes in physical behavior may affect the cardiovascular parameters (CVPs) of older adults. Methods: Participants wore a thigh-mounted accelerometer for 7 days. Phenotype of the carotid, brachial, and popliteal artery was conducted using ultrasound. Isotemporal substitution was used to simulate the degree to which replacing 1 hour of physical behavior with another would affect CVP. Results: Substitution of sedentary behavior with Standing and sporadic moderate- to vigorous-intensity physical activity (MVPA accumulated in bouts <10 min) would reduce resting heart rate [-6.20 beats per minute (-12.1 to -0.22) and -3.72 beats per minute (-7.01 to -0.44), respectively]. Substitution of sedentary behavior with light-intensity physical activity would reduce carotid artery diameter [-0.54 mm (-1.00 to -0.07)]. Substitution of Standing with sporadic MVPA would increase popliteal artery diameter [1.31 mm (0.11 to 2.51)]. Conclusions: Our modeling suggests that an accumulation of MVPA bouts that are shorter than the recommended 10-minute minimum may still improve CVP, with lower intensity physical activity also influencing CVP. Our findings are a promising avenue for lifestyle interventions in older adults to reduce the aging effects on CVP for those who cannot engage or sustain sufficient MVPA. [ABSTRACT FROM AUTHOR]

Background: Physical behavior [PB, physical activity (PA), and sedentary behavior (SB)] can adjust cardiovascular mortality risk in older adults. The aim of this study was to predict cardiovascular parameters (CVPs) using 21 parameters of PB. Methods: Participants [n = 93, 73.8 (6.23) y] wore a thigh-mounted accelerometer for 7 days. Phenotype of the carotid, brachial, and popliteal arteries was conducted using ultrasound. Results: Sedentary behavior was associated with one of the 19 CVPs. Standing and light-intensity PA was associated with 3 and 1 CVP, respectively. Our prediction model suggested that an hourly increase in light-intensity PA would be negatively associated with popliteal intima-media thickness [0.09 mm (95% confidence interval, 0.15 to 0.03)]. sMVPA [moderate-vigorous PA (MVPA), accumulated in bouts <10 min] was associated with 1 CVP. 10MVPA (MVPA accumulated in bouts ≥=10 min) had no associations. W50% had associations with 3 CVP. SB%, alpha, true mean PA bout, daily sum of PA bout time, and total week 10MVPA each were associated with 2 CVP. Conclusions: Patterns of PB are more robust predictors of CVP than PB (hours per day). The prediction that popliteal intima-media thickness would be negatively associated with increased standing and light-intensity PA engagement suggests that older adults could obtain health benefits without MVPA engagement. [ABSTRACT FROM AUTHOR]

Sedentary behavior (SB) has emerged as an independent public-health risk and may contribute to the lower bone mineral density (BMD) in old (>60 years of age) than young adults. The purpose of this study was to quantify SB and habitual physical behavior (PB) in community-dwelling older adults and how this correlates with BMD. In 112 relatively healthy and independent-living individuals aged 72.5 ± 6.4 years, BMD, PB and SB were determined using dual energy X-ray absorptiometry and 7-day three-dimensional accelerometry, respectively. In men, only healthy and osteopenic BMDs were found, whereas in women, osteoporotic BMD classifications also occurred. Our sample spent ∼61%, 7%, 12% and 19% of daily waking hours in SB, standing, LIPA [light intensity physical activity (PA)] and MVPA (medium-to-vigorous intensity PA), respectively. In men, after accounting for covariates (BMI, total fat, android:gynoid ratio), sleeping (hours/day), number of breaks in SB, number of SB ≥ 5 min, number of PA bouts, total duration of PA bouts (min), mean PA bouts duration (min), LIPA (%PA bout time) and MVPA (%PA bout time) were all predictors of BMD. In women, after accounting for covariates (age, BMI, total fat, android:gynoid ratio), SB (hours/day), SB (% waking hours), LIPA (hours/day), LIPA (% waking hours), MVPA (% waking hours) and number of short SB (i.e., <5 min), total time spent in PA (min) significantly correlated with BMD. In conclusion, the PB predictors of bone health in older persons include: night time sleeping duration, number of short bouts of SB, number and duration of bouts of PA relative to total waking hours. While radar graphs of PB patterns for healthy, osteopenic, osteoporotic individuals highlighted significant differences in PB between them, they were not consistent with the expectations from the Mechanostat Theory: i.e., more loading leads to better bone. Rather, our results suggest that a balance of activities must be maintained across the PB spectrum, where certain PB parameters are especially impactful in each sex, supporting the recently coined multifactorial-based variations in the Mechanostat threshold. [ABSTRACT FROM AUTHOR]