Mobile technology has become a ubiquitous technology and can be particularly useful in the delivery of health interventions. This technology can allow us to deliver interventions to scale, cover ...broad geographic areas, and deliver technologies in highly tailored ways based on the preferences or characteristics of users. The broad use of mobile technologies supports the need for usability assessments of these tools. Although there have been a number of usability assessment instruments developed, none have been validated for use with mobile technologies.
The goal of this work was to validate the Health Information Technology Usability Evaluation Scale (Health-ITUES), a customizable usability assessment instrument in a sample of community-dwelling adults who were testing the use of a new mobile health (mHealth) technology.
A sample of 92 community-dwelling adults living with HIV used a new mobile app for symptom self-management and completed the Health-ITUES to assess the usability of the app. They also completed the Post-Study System Usability Questionnaire (PSSUQ), a widely used and well-validated usability assessment tool. Correlations between these scales and each of the subscales were assessed.
The subscales of the Health-ITUES showed high internal consistency reliability (Cronbach alpha=.85-.92). Each of the Health-ITUES subscales and the overall scale was moderately to strongly correlated with the PSSUQ scales (r=.46-.70), demonstrating the criterion validity of the Health-ITUES.
The Health-ITUES has demonstrated reliability and validity for use in assessing the usability of mHealth technologies in community-dwelling adults living with a chronic illness.
Abstract
Versatile methods to organize proteins in space are required to enable complex biomaterials, engineered biomolecular scaffolds, cell-free biology, and hybrid nanoscale systems. Here, we ...demonstrate how the tailored encapsulation of proteins in DNA-based voxels can be combined with programmable assembly that directs these voxels into biologically functional protein arrays with prescribed and ordered two-dimensional (2D) and three-dimensional (3D) organizations. We apply the presented concept to ferritin, an iron storage protein, and its iron-free analog, apoferritin, in order to form single-layers, double-layers, as well as several types of 3D protein lattices. Our study demonstrates that internal voxel design and inter-voxel encoding can be effectively employed to create protein lattices with designed organization, as confirmed by in situ X-ray scattering and cryo-electron microscopy 3D imaging. The assembled protein arrays maintain structural stability and biological activity in environments relevant for protein functionality. The framework design of the arrays then allows small molecules to access the ferritins and their iron cores and convert them into apoferritin arrays through the release of iron ions. The presented study introduces a platform approach for creating bio-active protein-containing ordered nanomaterials with desired 2D and 3D organizations.
Although the current vehicle detection and recognition framework based on deep learning has its own characteristics and advantages, it is difficult to effectively combine multi-scale and multi ...category vehicle features, and there is still room for improvement in vehicle detection and recognition performance. Based on this, an improved fast R-CNN convolutional neural network is proposed to detect dim targets in complex traffic environment. The deep learning model of fast R-CNN convolutional neural network is introduced into the image recognition of complex traffic environment, and a structure optimization method is proposed, which replaces VGG16 in fast RCNN with RESNET to make it suitable for small target recognition in complex background. Max pooling is the down sampling method, and then feature pyramid network is introduced into RPN to generate target candidate box to optimize the structure of convolutional neural network. After training with 1497 images, the complex traffic environment images are identified and tested. The results show that the accuracy of the proposed method is better than other comparison methods, and the highest accuracy is 94.7%.
A single-molecule three-dimensional (3D) structure is essential for understanding the thermal vibrations and dynamics as well as the conformational changes during the chemical reaction of ...macromolecules. Individual-particle electron tomography (IPET) is an approach for obtaining a snap-shot 3D structure of an individual macromolecule particle by aligning the tilt series of electron tomographic (ET) images of a targeted particle through a focused iterative 3D reconstruction method. The method can reduce the influence on the 3D reconstruction from large-scale image distortion and deformation. Due to the mechanical tilt limitation, 3D reconstruction often contains missing-wedge artifacts, presented as elongation and an anisotropic resolution. Here, we report a post-processing method to correct the missing-wedge artifact. This low-tilt tomographic reconstruction (LoTToR) method contains a model-free iteration process under a set of constraints in real and reciprocal spaces. A proof of concept is conducted by using the LoTToR on a phantom, i.e., a simulated 3D reconstruction from a low-tilt series of images, including that within a tilt range of ±15°. The method is validated by using both negative-staining (NS) and cryo-electron tomography (cryo-ET) experimental data. A significantly reduced missing-wedge artifact verifies the capability of LoTToR, suggesting a new tool to support the future study of macromolecular dynamics, fluctuation and chemical activity from the viewpoint of single-molecule 3D structure determination.
Objectives
We aimed to investigate the feasibility of predicting high-risk cytogenetic abnormalities (HRCAs) in patients with multiple myeloma (MM) using a spinal MRI-based radiomics method.
...Materials and methods
In this retrospective study, we analyzed the radiomic features of 248 lesions (HRCA
n
= 111 and non-HRCA
n
= 137) using T1WI, T2WI, and fat suppression T2WI. To construct the radiomics model, the top nine most frequent radiomic features were selected using logistic regression (LR) machine-learning processes. A combined LR model incorporating radiomic features and basic clinical characteristics (age and sex) was also built. Fivefold external cross-validation was performed, and a comparative analysis of 10 random fivefold cross-validation sets was used to verify result stability. Model performance was compared by plotting receiver operating characteristic curves and the area under the curve (AUC).
Results
Comparable AUC values were observed between the radiomics model and the combined model in validation cohorts (AUC: 0.863 vs. 0.870, respectively,
p
= 0.206). The radiomics model had an AUC of 0.863, with a sensitivity of 0.789, a specificity of 0.787, a positive predictive value of 0.753, a negative predictive value of 0.824, and an accuracy of 0.788 in the validation cohort, which were comparable with the performance in the training cohorts.
Conclusions
Radiomic features of routine spinal MRI reflect differences between HRCAs and non-HRCAs in patients with MM. This MRI-based radiomics model might be a useful and independent tool to predict HRCAs in patients MM.
Background
Primary care practices increasingly rely on the growing workforce of nurse practitioners (NPs) to meet primary care demand. Understanding teamwork between NPs and physicians in primary ...care practices is critically important.
Objective
We assessed teamwork between NPs and physicians practicing within the same primary care practice and determined how teamwork affects their job satisfaction, intent to leave their current job, and quality of care.
Design
A cross-sectional survey design was used to collect data from both NPs and physicians in New York State in 2017.
Participants
584 participants (398 NPs and 186 physicians) from 476 primary care practices completed the survey yielding a 27% response rate for NPs and 12% for physicians.
Main Measures
The survey tool contained validated measures of teamwork and three outcomes: job satisfaction, intent to leave, and perceived quality of care. Simple and multi-level multivariable regression models were built.
Key Results
Most participants (76%) were either moderately satisfied or very satisfied with their job (NP sample: 75%; physician sample: 77%) and about 10% intended to leave their current job (NP sample: 11%; physician sample: 9%). The average perceived quality of care was the same across NP and physician samples with a mean of 8.5 on a 11 point scale. After controlling for confounders, a higher organizational-level teamwork score was associated with higher job satisfaction (cumulative OR: 3.00; 95% CI: 1.85-4.88), lower odds of intent to leave (OR: 0.25; 95% CI: 0.09-0.74), and higher perceived quality of care (b=1.00; 95% CI: 0.77-1.23).
Conclusions
This study produced evidence about NP-physician teamwork in primary care practices. We found the vast majority of NPs and physicians reported favorable teamwork, and that teamwork affects clinician job satisfaction and intent to leave as well as perceived quality of care in their practices.
Objectives
This study aimed to use the most frequent features to establish a vertebral MRI-based radiomics model that could differentiate multiple myeloma (MM) from metastases and compare the model ...performance with different features number.
Methods
We retrospectively analyzed conventional MRI (T1WI and fat-suppression T2WI) of 103 MM patients and 138 patients with metastases. The feature selection process included four steps. The first three steps defined as conventional feature selection (CFS), carried out 50 times (ten times with 5-fold cross-validation), included variance threshold, SelectKBest, and least absolute shrinkage and selection operator. The most frequent fixed features were selected for modeling during the last step. The number of events per independent variable (EPV) is the number of patients in a smaller subgroup divided by the number of radiomics features considered in developing the prediction model. The EPV values considered were 5, 10, 15, and 20. Therefore, we constructed four models using the top 16, 8, 6, and 4 most frequent features, respectively. The models constructed with features selected by CFS were also compared.
Results
The AUCs of 20EPV-Model, 15EPV-Model, and CSF-Model (AUC = 0.71, 0.81, and 0.78) were poor than 10EPV-Model (AUC = 0.84,
p
< 0.001). The AUC of 10EPV-Model was comparable with 5EPV-Model (AUC = 0.85,
p
= 0.480).
Conclusions
The radiomics model constructed with an appropriate small number of the most frequent features could well distinguish metastases from MM based on conventional vertebral MRI. Based on our results, we recommend following the 10 EPV as the rule of thumb for feature selection.
Key Points
•
The developed radiomics model could distinguish metastases from multiple myeloma based on conventional vertebral MRI.
•
An accurate model based on just a handful of the most frequent features could be constructed by utilizing multiple feature reduction techniques.
•
An event per independent variable value of 10 is recommended as a rule of thumb for modeling feature selection.
•Underlying cardiovascular diseases increase the incidence and severity of coronavirus infection.•Cardiac injury is common in patients with COVID-19. It is a risk factor for mortality of COVID-19 ...patients.•Cardiovascular complications are mainly related to fever, inflammation, the direct effects of Covs and drug side effects.
The outbreak of coronavirus disease 2019 (COVID-19) has once again aroused people's concern about coronavirus. Seven human coronaviruses (HCoVs) have been discovered so far, including HCoV-229E, HCoV-NL63, HCoV-OC43, HCoV-HKU115, severe acute respiratory syndrome coronavirus, Middle East respiratory syndrome coronavirus and severe acute respiratory syndrome coronavirus 2. Existing studies show that the cardiovascular disease increased the incidence and severity of coronavirus infection. At the same time, myocardial injury caused by coronavirus infection is one of the main factors contributing to poor prognosis. In this review, the recent clinical findings about the relationship between coronaviruses and cardiovascular diseases and the underlying pathophysiological mechanisms are discussed. This review aimed to provide assistance for the prevention and treatment of COVID-19.
Background Patients in home health care (HHC), the fastest growing health care sector, are at risk for infection. The existing research on infection in HHC is often limited by small sample sizes, ...local scope of inquiry, and a lack of current data. There is no national study examining agency-level infection rates. Methods This secondary data analysis used a 20% random sample of the 2010 national Outcome and Assessment Information Set (OASIS) data. An infection case was identified when the HHC patient was hospitalized or received emergency care for respiratory infection, urinary tract infection, intravenous catheter-related infection, wound infection, or deterioration. Proportions of infection cases out of the total number of patients were calculated for the whole sample and for each HHC agency. Results The final analysis included 199,462 patients from 8,255 HHC agencies. Approximately 3.5% of patients developed infections during their HHC stay, leading to emergency care treatment or hospitalization. Seventeen percent of unplanned hospitalizations among HHC patients were caused by infections. The agency-level infection rate ranged from 0%-34%, with an average of 3.5%. Conclusion To our knowledge, this is the first study to examine the proportion of hospitalizations or emergency care treatment caused by infection in HHC and the agency-level infection rate at a national level by using OASIS data. These data demonstrate that infection is a serious problem in HHC, and infection rates varied between agencies. The variance in agency level rates may be caused by differences in infection control policies and practices. Better infection surveillance system in HHC is needed to benchmark quality of care.
Little is known about how engagement with healthcare providers mediates the relationship between psychosocial factors (anxiety, depression, stigma) and medication adherence among persons living with ...HIV (PLWH). Moreover, little research has investigated potential biological sex differences in this relationship. We conducted a secondary analysis of data collected from four projects (N = 281) focused on improving health outcomes in PLWH. Males displayed (a) negative association between depression and engagement with healthcare providers (
β
= − 0.02, z = − 3.20,
p
= 0.001) and (b) positive association between engagement with healthcare providers and medication adherence (
β
= 0.55, OR = 1.73, z = 2.62,
p
= 0.009). Females showed no association between any of these factors. Anxiety and stigma were not significantly associated with medication adherence. Path analysis modeling for males had a very good fit (CFI = 1, TLI = 1, RMSEA = 0); none of the regression coefficients was significant for females. The significant relationship between depression and medication adherence among males was fully mediated by engagement with healthcare providers. Findings suggest that adherence interventions for PLWH should be tailored by biological sex.
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