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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.

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.

This literature review focuses on aspects of sedentary behaviour (SB) in elderly. Since it has been identified as a distinct health risk, independent of physical activity, SB is a significant issue. This is particularly true for an ageing population as evidence shows that older adults (aged ≥65 years) are the most sedentary age group (on average 8.5-9.6 h daily sitting time). Accurate SB assessment is important for understanding this habitual behaviour and its impact. However, SB measurement is challenging, regardless of the method used. Although negative associations of SB in elderly have been reported for several health outcomes, evidence is inconclusive, apart from the evidence on the adverse SB effect on the all-cause mortality rate. Generally, strategies have been proposed to counteract SB, of which breaking prolonged sedentary bouts with at least light-intensity physical activity seems to be the most promising. Overall, further research in elderly is required to increase the evidence and to either support or refute the current findings. Moreover, further research will help to develop informed SB guidelines for an optimal strategy to counteract SB and its health effects in older adults.

Prolonged sedentary behaviour (SB) i.e. longer bouts, is suggested to have a range of negative health effects, independent of habitual light and medium-to-vigorous physical activity (LIPA or MVPA). Any effect on musculoskeletal size, architecture or morphology has seldom been reported in older adults. Moreover, no study has yet determined if any association would persist following adjustment for covariates. Therefore, the aim of the present study was to investigate the associations between SB, and properties of the Gastrocnemius Medialis (GM) muscle, in a cross-sectional sample of older adults using compositional data analysis. 105 healthy older adults (73±6y) wore a thigh mounted tri-axial accelerometer for seven consecutive days, and underwent ultrasound [e.g. muscle length (Lm), anatomical cross-sectional area (ACSA), muscle volume (VM), fascicle length (LF), & physiological cross-sectional area (PCSA)], body composition (e.g. DEXA) and health (e.g. medical history) assessments. In-unadjusted models, SB time was negatively associated with ACSA at 75% of Lm (R2adj = 0.085), VM (R2adj = 0.020), and PCSA (R2adj = 0.039). Standing was positively associated with pennation angle (R2adj = 0.110), which persisted following co-variate adjustment (R2adj = 0.296). In fully adjusted models, both SB & LIPA time were associated with ACSA at 75% of Lm (Both R2adj = 0.393). Standing and light activity time were also associated with LF, VM, & PCSA (R2adj 0.116–0.573). In fully adjusted models, SB pattern parameters (i.e. the manner in which sedentary behaviour is accumulated daily throughout waking hours such as the timing, duration and frequency of sedentary bouts), were associated with GM muscle properties (R2adj 0.156–0.564) including LM, LF, and VM. The pattern, rather than accumulated daily SB time, was associated with the size and architecture of the GM. Our results suggest that regardless of co-existing habitual physical activities, SB bouts should be kept short and frequently interrupted to offset some of the deleterious ageing-related muscle architecture characteristics changes. [ABSTRACT FROM AUTHOR]

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. [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]

Tools for objective monitoring of home-based training and physical behavior (PB) in (pre)sarcopenic older adults are needed. The present study explored two approaches with machine learning models, including accelerometer data either with or without additional gyroscope data (in an inertial measurement unit). Twenty-five community-dwelling (pre)sarcopenic adults mean 80.7 [5.5] years) performed the Otago exercise protocol (OEP) and PB modules (e.g., sitting or walking) while wearing an inertial measurement unit on the lower back (Dynaport MoveMonitor; McRoberts, The Hague, The Netherlands). Machine learning (ML) models for classification were developed by two approaches (top-down and bottom-up approaches) and with two levels of classification: general (no wear, OEP, and PB) and detailed (all subclassifications). Classification output was compared with video recordings. For the bottom-up approach, one ML model was developed. For the top-down approach, data were first classified as no wear, OEP, or PB. Thereafter, OEP and PB were subclassified by one ML model each into subclassification. Only classification of the general classification no wear and the subclassification sitting/lying reached the acceptable performance threshold of 80%. This result was independent of the approach used. Moreover, a gyroscope did not improve performance. Despite the progress in ML techniques and monitors, objective compliance registrations remain challenging. [ABSTRACT FROM AUTHOR]

Background: Studies have seldom used Compositional Data Analysis (CoDA) to map the effects of sleep, sedentary behaviour, and physical activity on older adults' cardio-metabolic profiles. This study therefore aimed to illustrate how sleep, sedentary behaviour, and physical activity profiles differ between older adult groups (60–89 years), with 'low' compared to those with 'high' concentrations of endocrine cardio-metabolic disease risk markers, using CoDA. Method: Ninety-three participants (55% female) wore a thigh-mounted triaxial accelerometer for seven consecutive free-living days. Accelerometer estimates of daily average hours of engagement in sedentary behaviour (SB), standing, light-intensity physical activity (LIPA), sporadic moderate-vigorous physical activity (sMVPA, accumulated with bouts between 1 and 10 min), 10-min moderate-vigorous physical activity (10MVPA, accumulated with bouts ≥10 min), in addition to self-reported sleeping hours were reported. Fasted whole blood concentrations of total cholesterol, triglyceride, glucose, and glycated haemoglobin, and serum lipoprotein lipase (LPL), interleukin-6 (IL-6), and procollagen III N-terminal propeptide were determined. Results: Triglyceride concentration appeared to be highly dependent on 10MVPA engagement as the 'low' and 'high' concentration groups engaged in 48% more and 32% less 10MVPA, respectively, relative to the geometric mean of the entire study sample. Time-use composition of the 'low' LPL group's engagement in 10MVPA was 26% less, while the 'high' LPL group was 7.9% more, than the entire study sample. Time-use composition of the 'high' glucose and glycated haemoglobin groups appeared to be similar as both engaged in more Sleep and SB, and less 10MVPA compared to the study sample. Participants with a 'low' IL-6 concentration engaged in 4.8% more Sleep and 2.7% less 10MVPA than the entire study sample. Time-use composition of the Total Cholesterol groups was mixed with the 'low' concentration group engaging in more Standing and 10MVPA but less Sleep, SB, LIPA, and sMVPA than the entire study sample. Conclusion: Older adults should aim to increase 10MVPA engagement to improve lipid profile and decrease SB engagement to improve glucose profile. [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]

Following publication of the original article [1], the authors reported an error on the content of Availability of data and materials section in their paper. [ABSTRACT FROM AUTHOR]