Search

A deep learning model was developed to identify osteoporosis from chest X-ray (CXR) features with high accuracy in internal and external validation. It has significant prognostic implications, identifying individuals at higher risk of all-cause mortality. This Artificial Intelligence (AI)-enabled CXR strategy may function as an early detection screening tool for osteoporosis. The aim of this study was to develop a deep learning model (DLM) to identify osteoporosis via CXR features and investigate the performance and clinical implications. This study collected 48,353 CXRs with the corresponding T score according to Dual energy X-ray Absorptiometry (DXA) from the academic medical center. Among these, 35,633 CXRs were used to identify CXR- Osteoporosis (CXR-OP). Another 12,720 CXRs were used to validate the performance, which was evaluated by the area under the receiver operating characteristic curve (AUC). Furthermore, CXR-OP was tested to assess the long-term risks of mortality, which were evaluated by Kaplan‒Meier survival analysis and the Cox proportional hazards model. The DLM utilizing CXR achieved AUCs of 0.930 and 0.892 during internal and external validation, respectively. The group that underwent DXA with CXR-OP had a higher risk of all-cause mortality (hazard ratio [HR] 2.59, 95% CI: 1.83–3.67), and those classified as CXR-OP in the group without DXA also had higher all-cause mortality (HR: 1.67, 95% CI: 1.61–1.72) in the internal validation set. The external validation set produced similar results. Our DLM uses CXRs for early detection of osteoporosis, aiding physicians to identify those at risk. It has significant prognostic implications, improving life quality and reducing mortality. AI-enabled CXR strategy may serve as a screening tool. [ABSTRACT FROM AUTHOR]

This study describes moderate to vigorous physical activity (MVPA) and sedentary behavior among New York City (NYC) residents 60 years and older and compared with national United States' estimates. Adults aged 60 or older living in NYC (n = 760) were compared with similar aged adults from the National Health and Nutrition Examination Survey (NHANES; n = 2,451 adults). Both groups wore an ActiGraph accelerometer for one week. The NYC sample recorded 13.2, 23.8, and 37.8 mean min/day of MVPA and the NHANES sample recorded 10.6, 21.1, and 39.3, depending on the definition. Sedentary behavior averaged 9.6 hr/day for the NYC sample and 9.3 hr/day for the NHANES sample. The NYC sample spent a longer proportion of time in sedentary behavior and light activities, but more time in MVPA than the NHANES sample. Urbanicity may explain some of the differences between the two samples. [ABSTRACT FROM AUTHOR]

The US physical activity (PA) recommendations were based primarily on studies in which self-reported data were used. Studies that include accelerometer-assessed PA and sedentary behavior can contribute to these recommendations. In the present study, we explored the associations of PA and sedentary behavior with all-cause and cardiovascular disease (CVD) mortality in a nationally representative sample. Among the 2003-2006 National Health and Nutrition Examination Survey cohort, 3,809 adults 40 years of age or older wore an accelerometer for 1 week and self-reported their PA levels. Mortality data were verified through 2011, with an average of 6.7 years of follow-up. We used Cox proportional hazards models to obtain adjusted hazard ratios and 95% confidence intervals. After excluding the first 2 years, there were 337 deaths (32% or 107 of which were attributable to CVD). Having higher accelerometer-assessed average counts per minute was associated with lower all-cause mortality risk: When compared with the first quartile, the adjusted hazard ratio was 0.37 (95% confidence interval: 0.23, 0.59) for the fourth quartile, 0.39 (95% confidence interval: 0.27, 0.57) for the third quartile, and 0.60 (95% confidence interval: 0.45, 0.80) second quartile. Results were similar for CVD mortality. Lower all-cause and CVD mortality risks were also generally observed for persons with higher accelerometer-assessed moderate and moderate-to-vigorous PA levels and for self-reported moderate-to-vigorous leisure, household and total activities, as well as for meeting PA recommendations. Accelerometer-assessed sedentary behavior was generally not associated with all-cause or CVD mortality in fully adjusted models. These findings support the national PA recommendations to reduce mortality. [ABSTRACT FROM AUTHOR]

Purpose: Parks offer a free option for physical activity in many communities. How much time people spend using parks and the contribution that parks makes to their physical activity is not known. This study describes patterns of park use and physical activity among a diverse adult sample. Methods: From five US states, 248 adults enrolled in or near 31 study parks. Participants wore a global positioning system (GPS) monitor (Qstarz BT-Q1000X) and an ActiGraph accelerometer (GT1M) concurrently for 3 wk. Parks were mapped from local and national park shape files. Park visits and travel to and from the parks were derived from the objective data. Results: Participants visited parks a median of 2.3 times per week, and park visits lasted a median of 42.0 min. Overall, participants engaged in a median of 21.7 min•d-1 of moderate activity and 0.1 min•d-1 of vigorous activity, with an average of 8.2% of all moderate and 9.4% of all vigorous activity occurring within the parks. Among those with at least one park visit (n = 218), counts per minute, moderate, moderate-to-vigorous physical activity (MVPA), number and time in MVPA bouts per day, and sedentary behavior were all higher on days when a park was visited compared with days when a park was not visited. Considering several definitions of active travel, walking or bicycling to and from the park added an additional 3.7-6.6 mean minutes of MVPA per park visit. Conclusions: Parks contributed as a place and destination for physical activity but were underused. One of the next steps in this line of inquiry is to understand characteristics of parks used more often as a place and destination for physical activity. [ABSTRACT FROM AUTHOR]

Objective: Polycyclic aromatic hydrocarbons (PAHs) are not only natural but also anthropogenic contaminants that exist in many places in the environment. Human beings often accidentally ingest PAHs via smoking. Furthermore, smoking may increase the risk of bowel disorder, including diarrhea and other gastrointestinal problems. Therefore, PAH exposure is hypothesized to be related to diarrhea risk. This study discusses the association between diarrhea and PAH exposure in the United States adult population. Method: 10,537 participants from the National Health and Nutrition Examination Survey (NHANES 2001–2006) were involved in this cross-sectional analysis. Bowel disorders were assessed via examination of stool frequency and stool type. The concentrations of urinary PAH metabolites were used to evaluate PAH exposure. The association between bowel habits and PAH exposure was assessed using a multivariate logistic regression model with covariate assessment of gender, age, race, liver function, kidney function, and common chronic health diseases. Results: All PAH metabolites except 1-hydroxynaphthalene, 1-hydroxypyrene, and 9-hydroxyfluorene were substantially correlated with an increased risk of diarrhea (p < 0.05) after modification of relevant covariables. This study also revealed significant association in the group of females (p < 0.05). Furthermore, all PAH metabolites except 1-hydroxynaphthalene, 2-hydroxyphenanthrene, 1-hydroxypyrene, and 9-hydroxyfluorene show significantly positive association in the non-obesity group (BMI < 30, p < 0.05). Conclusions: PAH exposure is highly associated with risk of bowel disorders among the adult population in the United States, especially in female and non-obesity populations. More research is necessary to shed light on the pathophysiological mechanisms associated to PAH exposure and diarrhea. [ABSTRACT FROM AUTHOR]