Introduction Physical activity is suggested to be important for low back pain (LBP) but a major problem is the limited validity of the measurement of physical activities, which is usually based on questionnaires. Physical fitness can be viewed as a more objective measurement and our question was how physical activity based on self-reports and objective measured levels of physical fitness were associated with LBP. Materials and methods We analyzed cross-sectional data of 1,723 police employees. Physical activity was assessed by questionnaire (SQUASH) measuring type of activity, intensity, and time spent on these activities. Physical fitness was based on muscular dynamic endurance capacity and peak oxygen uptake (VO2 peak). Severe LBP, interfering with functioning, was defined by pain ratings C4 on a scale of 0–10. Results Higher levels of physical fitness, both muscularand aerobic, were associated with less LBP (OR: 0.54; 95%CI: 0.34–0.86, respectively, 0.59: 95%CI: 0.35–0.99). For self-reported physical activity, both a low and a high level of the total physical activity pattern were associated with an increase of LBP (OR: 1.52; 95%CI: 1.00–2.31, respectively, 1.60; 95%CI: 1.05–2.44). Conclusion These findings suggest that physical activity of an intensity that improves physical fitness may be important in the prevention of LBP
BackgroundMechanical ventilation affects the respiratory muscles, but little is known about long-term recovery of respiratory muscle weakness (RMW) and potential associations with physical functioning in survivors of critical illness. The aim of this study was to investigate the course of recovery of RMW and its association with functional outcomes in patients who received mechanical ventilation.MethodsWe conducted a prospective cohort study with 6-month follow-up among survivors of critical illness who received ≥ 48 hours of invasive mechanical ventilation. Primary outcomes, measured at 3 timepoints, were maximal inspiratory and expiratory pressures (MIP/MEP). Secondary outcomes were functional exercise capacity (FEC) and handgrip strength (HGS). Longitudinal changes in outcomes and potential associations between MIP/MEP, predictor variables, and secondary outcomes were investigated through linear mixed model analysis.ResultsA total of 59 participants (male: 64%, median age [IQR]: 62 [53–66]) were included in this study with a median (IQR) ICU and hospital length of stay of 11 (8–21) and 35 (21–52) days respectively. While all measures were well below predicted values at hospital discharge (MIP: 68.4%, MEP 76.0%, HGS 73.3% of predicted and FEC 54.8 steps/2m), significant 6-month recovery was seen for all outcomes. Multivariate analyses showed longitudinal associations between older age and decreased MIP and FEC, and longer hospital length of stay and decreased MIP and HGS outcomes. In crude models, significant, longitudinal associations were found between MIP/MEP and FEC and HGS outcomes. While these associations remained in most adjusted models, an interaction effect was observed for sex.ConclusionRMW was observed directly after hospital discharge while 6-month recovery to predicted values was noted for all outcomes. Longitudinal associations were found between MIP and MEP and more commonly used measures for physical functioning, highlighting the need for continued assessment of respiratory muscle strength in deconditioned patients who are discharged from ICU. The potential of targeted training extending beyond ICU and hospital discharge should be further explored.
MULTIFILE
Background: Manual muscle mass assessment based on Computed Tomography (CT) scans is recognized as a good marker for malnutrition, sarcopenia, and adverse outcomes. However, manual muscle mass analysis is cumbersome and time consuming. An accurate fully automated method is needed. In this study, we evaluate if manual psoas annotation can be substituted by a fully automatic deep learning-based method.Methods: This study included a cohort of 583 patients with severe aortic valve stenosis planned to undergo Transcatheter Aortic Valve Replacement (TAVR). Psoas muscle area was annotated manually on the CT scan at the height of lumbar vertebra 3 (L3). The deep learning-based method mimics this approach by first determining the L3 level and subsequently segmenting the psoas at that level. The fully automatic approach was evaluated as well as segmentation and slice selection, using average bias 95% limits of agreement, Intraclass Correlation Coefficient (ICC) and within-subject Coefficient of Variation (CV). To evaluate performance of the slice selection visual inspection was performed. To evaluate segmentation Dice index was computed between the manual and automatic segmentations (0 = no overlap, 1 = perfect overlap).Results: Included patients had a mean age of 81 ± 6 and 45% was female. The fully automatic method showed a bias and limits of agreement of -0.69 [-6.60 to 5.23] cm2, an ICC of 0.78 [95% CI: 0.74-0.82] and a within-subject CV of 11.2% [95% CI: 10.2-12.2]. For slice selection, 84% of the selections were on the same vertebra between methods, bias and limits of agreement was 3.4 [-24.5 to 31.4] mm. The Dice index for segmentation was 0.93 ± 0.04, bias and limits of agreement was -0.55 [1.71-2.80] cm2.Conclusion: Fully automatic assessment of psoas muscle area demonstrates accurate performance at the L3 level in CT images. It is a reliable tool that offers great opportunities for analysis in large scale studies and in clinical applications.
