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BackgroundRegular physical activity (PA) contributes to the primary and secondary prevention of several chronic diseases and reduces the risk of premature death. Physical inactivity is a modifiable risk factor for cardiovascular disease and a variety of chronic disorders such as diabetes, obesity, hypertension, bone and joint diseases (eg, osteoporosis and osteoarthritis), depression, and colon and breast cancer. Population aging and the related increase in chronic diseases have a major impact on the health care systems of most Western countries and will produce an even more significant effect in the future. Monitoring PA is a valuable method of determining whether people are performing enough PA so as to prevent chronic diseases or are showing early symptoms of those diseases. ObjectiveThe aim of this study was to estimate the accuracy of wearable devices in quantifying the PA of elderly people in a real-life setting. MethodsParticipants aged 70 to 90 years with the ability to walk safely without any walking aid for at least 300 meters, who had no walking disabilities or episodes of falling while walking in the last 12 months, were asked to walk 150 meters at their preferred pace wearing a vívoactive HR device (Garmin Ltd) and actual steps were monitored and tallied by a researcher using a hand-tally counter to assess the performance of the device at a natural speed. A Bland-Altman plot was used to analyze the difference between manually counted steps and wearable device–measured steps. The intraclass correlation coefficient (ICC) was computed (with a 95% confidence interval) between step measurements. The generalized linear mixed-model (GLMM) ICCs were estimated, providing a random effect term (random intercept) for the individual measurements (gold standard and device). Both adjusted and conditional ICCs were computed for the GLMM models considering separately the effect of age, sex, BMI, and obesity. Analyses were performed using R software (R Foundation for Statistical Computing) with the rms package. ResultsA total of 23 females and 26 males were enrolled in the study. The median age of the participants was 75 years. The Bland-Altman plot revealed that, excluding one observation, all differences across measurements were in the confidence bounds, demonstrating the substantial agreement between the step count measurements. The results were confirmed by an ICC equal to .98 (.96-.99), demonstrating excellent agreement between the two sets of measurements. ConclusionsThe level of accuracy of wearable devices in quantifying the PA of elderly people in a real-life setting that was found in this study supports the idea of considering wrist-wearable nonmedical devices (widely available in nonspecialized stores) as reliable tools. Both health care professionals and informal caregivers could monitor the level of PA of their patients.

BackgroundIn last few decades, several tools have been developed to measure physical function objectively; however, their use has not been well established in clinical practice. ObjectiveThis study aims to describe the preoperative physical function and to assess and compare 6-month postoperative changes in the physical function of patients undergoing treatment for aortic stenosis with either surgical aortic valve replacement (SAVR) or transcatheter aortic valve replacement (TAVR). The study also aims to evaluate the feasibility of wearable devices in assessing physical function in such patients. MethodsThis is a prospective observational study. The enrollment will be conducted 1 month before patients’ SAVR/TAVR. Patients will be provided with the wearable device at baseline (activity tracker device, Garmin vívoactive 3). They will be trained in the use of the device, and they will be requested to wear it on the wrist of their preferred hand until 12 months after SAVR/TAVR. After baseline assessment, they will undergo 4 follow-up assessments at 1, 3, 6, and 12 months after SAVR/TAVR. At baseline and each follow-up, they will undergo a set of standard and validated tests to assess physical function, health-related quality of life, and sleep quality. ResultsThe ethics committee of Vicenza in Veneto Region in Italy approved the study (Protocol No. 943; January 4, 2019). As of October 2020, the enrollment of participants is ongoing. ConclusionsThe use of the wearable devices for real-time monitoring of physical activity of patients undergoing aortic valve replacement is a promising opportunity for improving the clinical management and consequently, the health outcomes of such patients. Trial RegistrationClinicaltrials.gov NCT03843320; https://tinyurl.com/yyareu5y International Registered Report Identifier (IRRID)DERR1-10.2196/20072

During the COVID-19 lockdown, a high prevalence of disruption in lifestyle habits was reported, especially concerning sleep habits. The present study aimed to explore the relationship between lifestyles and the COVID-19 lockdown on subjects living in Mexico. A cross-sectional survey was conducted. An online questionnaire was administered to survey participants. A clustering procedure was performed to identify groups of survey respondents with similar characteristics. There were 155 survey respondents. Most of them were women (83%) of young age. The clustering identified two distinct groups of subjects, i.e., Cluster 1 and 2. The subjects in Cluster 2 were younger, more likely to use social media, and spent much more time watching TV/surfing the internet than subjects in Cluster 1. Furthermore, the prevalence of problems with falling and staying asleep during the lockdown was higher in Cluster 2 than in Cluster 1 (14% in Cluster 2 vs. 4% in Cluster 1) even though nobody from Cluster 2 had sleep problems before the lockdown. The study showed that younger respondents were those more affected by lockdown measures. [ABSTRACT FROM AUTHOR]

Demoralization comprises multiple dimensions. Among them, Subjective Incompetence (SI) is the perception of being incapable of appropriate action in demanding circumstances. SI may be an early sign of demoralization preceding hopelessness, thus we aimed at integrating items related to Subjective Incompetence into the clinical assessment of demoralization. We assessed 414 subjects from the general population with the Demoralization Scale, 24 items (DS24) and the Subjective Incompetence Scale, 12 items (SIS12). We used multiple approaches to detect the optimal number of factors and their item structure, then conducted Bayesian Item Response Theory analyses to study item psychometric properties. Item Response Theory models were used to extrapolate latent severity ratings of clinical dimensions. We modelled the DS24 with five factors (Disheartenment, Sense of Failure, Helplessness, Irritability, Loss of Purpose) and the SIS12 with three (Subjective Incompetence, Inability to plan, Inability to Deal). The more complex IRT model had the best predictive value and helped to identify the items with better discrimination properties across the different dimensions. Twenty items were retained and used to develop the combined Demoralization and Subjective Incompetence Scale (DSIS20), which maintained high correlation with raw and latent trait scores of the longer versions. We combined selected items of the DS24 and the SIS12 to develop the DSIS20, a shorter assessment instrument that includes Subjective Incompetence as well as other clinical dimensions of demoralization. Further study may clarify if DSIS20 may be helpful for the early detection of demoralization. [ABSTRACT FROM AUTHOR]

Objectives: In Italy, asbestos was used intensively until its ban in 1992, which was extended for asbestos cement factories until 1994. The aim of this study was to evaluate the dose–response between asbestos exposure and asbestosis mortality across a pool of Italian occupational cohorts, taking into account the presence of competing risks. Methods: Cohorts were followed for vital status and the cause of death was ascertained by a linkage with mortality registers. Cause‐specific (CS) Cox‐regression models were used to evaluate the dose‐exposure relationship between asbestosis mortality and the time‐dependent cumulative exposure index (CEI) to asbestos. Fine and Gray regression models were computed to assess the effect of competing risks of death. Results: The cohort included 12,963 asbestos cement workers. During the follow‐up period (1960−2012), of a total of 6961 deaths, we observed 416 deaths attributed to asbestosis, 879 to lung cancer, 400 to primary pleural cancer, 135 to peritoneal cancer, and 1825 to diseases of the circulatory system. The CS model showed a strong association between CEI and asbestosis mortality. Dose–response models estimated an increasing trend in mortality even below a CEI of 25 ff/mL‐years. Lung cancer and circulatory diseases were the main competing causes of death. Conclusions: Asbestos exposure among Italian asbestos‐cement workers has led to a very high number of deaths from asbestosis and asbestos‐related diseases. The increasing risk trend associated with excess deaths, even at low exposure levels, suggests that the proposed limit values would not have been adequate to prevent disability and mortality from asbestosis. [ABSTRACT FROM AUTHOR]

Simple Summary: This study aimed to predict the Length of hospital Stay (LoS) after laparoscopic right hemicolectomy for colon cancer using machine learning techniques. Accurately forecasting LoS is crucial for improving patient care and hospital resource management. The researchers utilized data from two large Italian studies, CoDIG 1 and CoDIG 2, to train and validate various machine learning models. The Random Forest (RF) algorithm demonstrated the best internal performance, while the Support Vector Machine (SVM) outperformed in external validation. Key factors influencing LoS included the use of fast-track protocols, type of anastomosis, and drainage. These findings could help tailor postoperative care and optimize hospital resources, ultimately enhancing patient outcomes and operational efficiency. The evolution of laparoscopic right hemicolectomy, particularly with complete mesocolic excision (CME) and central vascular ligation (CVL), represents a significant advancement in colon cancer surgery. The CoDIG 1 and CoDIG 2 studies highlighted Italy's progressive approach, providing useful findings for optimizing patient outcomes and procedural efficiency. Within this context, accurately predicting postoperative length of stay (LoS) is crucial for improving resource allocation and patient care, yet its determination through machine learning techniques (MLTs) remains underexplored. This study aimed to harness MLTs to forecast the LoS for patients undergoing right hemicolectomy for colon cancer, using data from the CoDIG 1 (1224 patients) and CoDIG 2 (788 patients) studies. Multiple MLT algorithms, including random forest (RF) and support vector machine (SVM), were trained to predict LoS, with CoDIG 1 data used for internal validation and CoDIG 2 data for external validation. The RF algorithm showed a strong internal validation performance, achieving the best performances and a 0.92 ROC in predicting long-term stays (more than 5 days). External validation using the SVM model demonstrated 75% ROC values. Factors such as fast-track protocols, anastomosis, and drainage emerged as key predictors of LoS. Integrating MLTs into predicting postoperative LOS in colon cancer surgery offers a promising avenue for personalized patient care and improved surgical management. Using intraoperative features in the algorithm enables the profiling of a patient's stay based on the planned intervention. This issue is important for tailoring postoperative care to individual patients and for hospitals to effectively plan and manage long-term stays for more critical procedures. [ABSTRACT FROM AUTHOR]