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To improve quantification of individual responses to bed rest interventions, we analyzed peripheral quantitative computer tomography (pQCT) datasets of the lower leg of 76 participants, who took part in eight different bed rest studies. A newly developed statistical approach differentiated measurement uncertainty U Meas from between-subject-variation (BSV) and within-subject variation (WSV). The results showed that U Meas decreased 59.3% to 80% over the two decades of bed rest studies (p < 0.01), and that it was higher for muscles than for bones. The reduction of U Meas could be explained by improved measurement procedures as well as a higher standardization. The majority (59.1%) of the individual responses pc i exceeded the 95% confidence interval defined by U Meas , indicating significant and substantial BSV, which was greater for bones than for muscles, especially at the diaphyseal measurement sites. Non-significant to small positive inter-site correlations between bone sites, but very large positive inter-site correlation between muscle sites suggests that substantial WSV exists in the tibia bone, but much less so in the calf musculature. Furthermore, endocortical circumference, an indicator of the individual's bone geometry could partly explain WSV and BSV. These results demonstrate the existence of substantial bone BSV, and that it is partly driven by WSV, and likely also by physical activity and dietary habits prior to bed rest. In addition, genetic and epigenetic variation could potentially explain BSV, but not WSV. As to the latter, differences of bone characteristics and the bone resorption process could offer an explanation for its existence. The study has also demonstrated the importance of duplicate baseline measurements. Finally, we provide here a rationale for worst case scenarios with partly effective countermeasures in long-term space missions. [ABSTRACT FROM AUTHOR]

Understanding differences between real-world walking speed (RWS) and laboratory-measured walking speed (LWS) is crucial for comprehensive mobility assessments, especially in context of prolonged immobilization. This study aimed to investigate disparities in walking speed following a 60-day bed-rest period. In 11 male participants, RWS was continuously monitored using a tri-axial accelerometer worn on the waist, while LWS was assessed via a 10-m walk test at preferred speed, on three different study days after immobilization. Statistical analyses included Bland–Altman and Pearson's correlation to evaluate agreement between RWS and LWS, alongside paired-sample t-tests and univariate linear regression models to assess significance of differences and temporal effects on gait speed. Results of Bland-Altman analysis showed no agreement between RWS and LWS (mean difference 0.77 m/s) and nonsignificant correlation (r=0.19, p-value=0.3). Paired-sample t-tests indicated significantly lower RWS compared to LWS for all study days (p-value <0.001). Univariate linear regression models demonstrated a significant effect of test day on RWS (p-value <0.001) but not on LWS (p-value=0.23). These findings emphasize the importance of integrating both assessments to capture comprehensive mobility changes following prolonged periods of inactivity. Particularly significant is that RWS is constantly lower than LWS, with the former being more representative as it reflects what normally participants would do when not under observation. Lastly, understanding discrepancies between RWS and LWS would allow for more appropriate rehabilitation programs to speed up recovery while simultaneously keeping the rehabilitation safe and tailored. [ABSTRACT FROM AUTHOR]

Assessing physical activity and cardiometabolic risk in masters athletes as an example of very high physical activity at old age. Forty-three men were studied in full factorial design, either as sprint or jump-trained masters athletes (MA, n=10, age 60–75 years), as young sprint or jump-trained athletes (YA, n=10, age 20–35 years), older control participants (OC, n=11, age 60–75 years) or as young control participants (AC, n=12, age 20–35 years). We performed bio-electrical impedance analysis and assessed serum markers of lipids and glucose metabolism and C-reactive protein, structured training hours, and habitual activity via mobile actimetry. Body fat was greater in OC than in MA (23.9 [SD 4.2] % vs. 14.0 [SD 5.7] %, p<0.001), and also greater than in YA and YC (both p<0.001). Weekly training hours were comparable between MA and YA (7.9 [SD3.3] hours vs. 11.1 [SD 4.8] hours, p=0.69). Habitual walking distance was greater in MA than in OC (7,387 [SD 4,923] m/day vs. 4,110 [SD 1,772] m/day, p=0.039), and so was habitual running distance (667 [SD690] m/day vs. 132 [427] m/day, p<0.001). HOMA-index was greater in OC than in MA (2.07 [SD 1.39] vs. 0.80 [SD 0.41], p=0.0039), and so was C-reactive protein (1.35 [SD 1.74] mg/l vs. 0.58 [SD 0.27] mg/ml, p=0.018), whereas serum lipids showed only moderate or no effect (all p between 0.036 and 0.07). Improved body composition and physical activity levels in MA are associated with lower cardiometabolic risk, which seems more pronounced for insulin sensitivity and inflammaging than for lipid metabolism. [ABSTRACT FROM AUTHOR]