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This study investigated the association between slip severity and pre-slip gait characteristics of younger and older subjects. Sixteen younger and eleven older healthy adults walked onto an unexpectedly slippery surface. Slip severity was categorized as either hazardous or non-hazardous using a 1.0 ms peak slip velocity threshold. The results showed that hazardous slips were associated with greater step lengths (normalized by leg length) (SLR), larger and more rapidly changing foot - floor angles (FFA) at heel strike, and increased cadence across the two subject groups. Older subjects were found to walk with shorter SLR and with smaller and more slowly changing FFA at heel strike compared to younger subjects. However, both younger and older subjects experienced hazardous slips at the same rate. A logistic regression model relating SLR and cadence to slip severity predicted that increased SLR and decreased cadence would result in increased probability of hazardous slip (R2 = 0.45, chi2 = 15.30, p<0.01). A second logistic regression model relating FFA with slip severity predicted that increased FFA would result in increased probability of hazardous slip (R2 = 0.53, chi2 = 16.55, p<0.01). These results suggest that gait characteristics prior to foot contact play an important role in the severity of an ensuing slip. The finding that older adults experienced hazardous slips at the same rate as young adults even though their SLR and FFA are smaller suggests that age is also playing a role in other aspects of postural control that impact slip severity.

The number of patients requiring tracheal intubation rose dramatically in March and April 2020 with the COVID-19 outbreak. Our thoracic surgery department has seen an increased incidence of severe pneumomediastinum referred for surgical opinion in intubated patients with COVID-19 pneumonitis. Here we present a series of five cases of severe pneumomediastinum requiring decompression therapy over a 7-day period in the current COVID-19 outbreak. We hypothesise that the mechanism for this is the aggressive disease pathophysiology with an increased risk of alveolar damage and tracheobronchial injury along with the use of larger bore tracheal tubes and higher ventilation pressures. We present this case series in order to highlight the increased risk of this potentially life-threatening complication among the COVID-19 patient cohort and offer guidance for its management to critical care physicians.

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Background-Anthropometry is a necessary aspect of aging-related research, especially in biomechanics and injury prevention. Little information is available on inertial parameters in the geriatric population that account for gender and obesity effects. The goal of this study was to report body segment parameters in adults aged 65 years and older, and to investigate the impact of aging, gender and obesity.Methods-Eighty-three healthy old (65-75 yrs) and elderly (>75 yrs) adults were recruited to represent a range of body types. Participants underwent a whole body dual energy x-ray absorptiometry scan. Analysis was limited to segment mass, length, longitudinal center of mass position, and frontal plane radius of gyration. A mixed-linear regression model was performed using gender, obesity, age group and two-way and three-way interactions (α=0.05).Findings-Mass distribution varied with obesity and gender. Males had greater trunk and upper extremity mass while females had a higher lower extremity mass. In general, obese elderly adults had significantly greater trunk segment mass with less thigh and shank segment mass than all others. Gender and obesity effects were found in center of mass and radius of gyration. Non-obese individuals possessed a more distal thigh and shank center of mass than obese. Interestingly, females had more distal trunk center of mass than males.Interpretation-Age, obesity and gender have a significant impact on segment mass, center of mass and radius of gyration in old and elderly adults. This study underlines the need to consider age, obesity and gender when utilizing anthropometric data sets.

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Objectives The predictive capabilities of skinfold regression equations are limited across populations and current equations may not be well suited for the prediction of body fat in older adults or obese Americans. The goal of this study was to compare percent body fat (%BF) predicted by several skinfold regression equations to %BF determined by Dual-Energy X-ray Absorptiometry (DXA) in obese and non-obese Caucasian men and women in the United states over the age of 65 years. Design A block design was used with two blocks: obesity (non-obese/obese) and gender (male/female). All subjects underwent the same testing procedures in one visit. Setting University of Pittsburgh Clinical and Translation Research Center. Participants Seventy-eight older healthy adults were recruited for participation. Measurements Actual percent body fat was determined from a whole body DXA scan. Estimated percent body fat (%BF) was calculated using skinfold measurements and established regression equations. The predictive accuracy of the regression equations was evaluated by comparing the estimated %BF to the actual %BF measured with DXA using a within subject ANOVA (α=0.05). This was done within subgroups: obese males, obese females, non-obese males and non-obese females. Results Durnin and Womersly and Jackson and Pollock had reasonably good agreement with DXA in older Caucasian American females and males, respectively. The remaining equations significantly overestimated %BF in older Caucasian American males. Mixed results were found in females with Gause-Nilsson and Jackson and Pollock significantly underestimating %BF, while Visser and Kwok overestimated %BF. Conclusion Numerous factors of a population including age, race, ethnicity, gender and obesity should be considered when selecting a skinfold regression equation to estimate %BF. While Durnin and Womersly and Jackson and Pollock are recommended for predicting %BF in older Caucasian American females and males, respectively, there exists a need to develop accurate regression models that consider obesity, gender, race or ethnicity when predicting %BF in a diverse geriatric American population.

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