Doughertyklavsen3776

in Sweden, was found to be acceptable and feasible in a general population.

The Arabic version of eHEALS, a unidimensional scale that is valid and reliable for measuring eHealth literacy among natively Arabic-speaking people in Sweden, was found to be acceptable and feasible in a general population.

The clinical mitigation of intracranial hypertension due to traumatic brain injury requires timely knowledge of intracranial pressure to avoid secondary injury or death. Noninvasive intracranial pressure (nICP) estimation that operates sufficiently fast at multihour timescales and requires only common patient measurements is a desirable tool for clinical decision support and improving traumatic brain injury patient outcomes. However, existing model-based nICP estimation methods may be too slow or require data that are not easily obtained.

This work considers short- and real-time nICP estimation at multihour timescales based on arterial blood pressure (ABP) to better inform the ongoing development of practical models with commonly available data.

We assess and analyze the effects of two distinct pathways of model development, either by increasing physiological integration using a simple pressure estimation model, or by increasing physiological fidelity using a more complex model. Comparison of the model onal model indicates that feedback between the systemic vascular network and nICP estimation scheme is crucial for modeling over long intervals. However, simple model reduction to ABP-only dependence limits its utility in cases involving other brain injuries such as ischemic stroke and subarachnoid hemorrhage. Additional methodologies and considerations needed to overcome these limitations are illustrated and discussed.

Mental health disorders affect multiple aspects of patients' lives, including mood, cognition, and behavior. Silmitasertib eHealth and mobile health (mHealth) technologies enable rich sets of information to be collected noninvasively, representing a promising opportunity to construct behavioral markers of mental health. Combining such data with self-reported information about psychological symptoms may provide a more comprehensive and contextualized view of a patient's mental state than questionnaire data alone. However, mobile sensed data are usually noisy and incomplete, with significant amounts of missing observations. Therefore, recognizing the clinical potential of mHealth tools depends critically on developing methods to cope with such data issues.

This study aims to present a machine learning-based approach for emotional state prediction that uses passively collected data from mobile phones and wearable devices and self-reported emotions. The proposed methods must cope with high-dimensional and heterogeneous tim mobile sensing data capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent valuable tools for clinicians to monitor patients' mood states.

These findings demonstrate the feasibility of designing machine learning models for predicting emotional states from mobile sensing data capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent valuable tools for clinicians to monitor patients' mood states.

Childhood obesity accompanied by lower levels of health-related physical fitness (HRPF) is a major threat to public health both internationally and locally. Children with intellectual disability, especially adolescents, have a higher risk of being overweight/obese and having poor HRPF levels. Therefore, more interventions are needed to help this population attain their optimal health levels. However, there has been relatively limited research on this population compared with on their typically developing peers.

The proposed study aims to fill this knowledge gap by developing and examining the success of a physical activity (PA) intervention for the target population.

The proposed study will be a 12-week, school-based randomized controlled trial. The participants (N=48) will be recruited from special schools for students with mild intellectual disability and then randomly allocated to either the intervention group (IG) or the wait-list control group (CG). During the intervention period, the participants 1.

The proposed study is expected to reduce obesity and improve HRPF levels in children with intellectual disability. If proven effective, the intervention will be made accessible to more special schools and mainstream schools with students with intellectual disability. Furthermore, the study can serve as an example for international researchers, policy makers, and members of the public who are seeking to tackle the problem of obesity and poor HRPF among children with intellectual disability.

ClinicalTrials.gov NCT04554355; https//www.clinicaltrials.gov/ct2/show/NCT04554355.

PRR1-10.2196/25838.

PRR1-10.2196/25838.

Limited consideration of clinical decision support (CDS) design best practices, such as a user-centered design, is often cited as a key barrier to CDS adoption and effectiveness. The application of CDS best practices is resource intensive; thus, institutions often rely on commercially available CDS tools that are created to meet the generalized needs of many institutions and are not user centered. Beyond resource availability, insufficient guidance on how to address key aspects of implementation, such as contextual factors, may also limit the application of CDS best practices. An implementation science (IS) framework could provide needed guidance and increase the reproducibility of CDS implementations.

This study aims to compare the effectiveness of an enhanced CDS tool informed by CDS best practices and an IS framework with a generic, commercially available CDS tool.

We conducted an explanatory sequential mixed methods study. An IS-enhanced and commercial CDS alert were compared in a cluster randomizedwere significantly higher than those of the commercial alert (62% vs 29% alerts adopted, P<.001; 14% vs 0% changed prescribing, P=.006). Of the 21 clinicians interviewed, most stated that they preferred the enhanced alert.

The results of this study suggest that applying CDS best practices with an IS framework to create CDS tools improves implementation success compared with a commercially available tool.

ClinicalTrials.gov NCT04028557; http//clinicaltrials.gov/ct2/show/NCT04028557.

ClinicalTrials.gov NCT04028557; http//clinicaltrials.gov/ct2/show/NCT04028557.

Multiple sclerosis (MS) is a chronic, neurodegenerative disease that causes a range of motor, sensory, and cognitive symptoms. Due to these symptoms, people with MS are at a high risk for falls, fall-related injuries, and reductions in quality of life. There is no cure for MS, and managing symptoms and disease progression is important to maintain a high quality of life. Mobile health (mHealth) apps are commonly used by people with MS to help manage their health. However, there are limited health apps for people with MS designed to evaluate fall risk. A fall risk app can increase access to fall risk assessments and improve self-management. When designing mHealth apps, a user-centered approach is critical for improving use and adoption.

The purpose of this study is to undergo a user-centered approach to test and refine the usability of the app through an iterative design process.

The fall risk app Steady-MS is an extension of Steady, a fall risk app for older adults. Steady-MS consists of 2 components a 2ealth apps for people with MS, it is important to prevent cognitive overload through simple and clear instructions and present scores that are understood and interpreted correctly through visuals and text. These findings underscore the importance of user-centered design and provide a foundation for the future development of tools to assess and prevent scalable falls for people with MS. Future steps include understanding the validity of the fall risk algorithm and evaluating the clinical utility of the app.

Smart technology use in rehabilitation is growing and can be used remotely to assist clients in self-monitoring their performance. With written home exercise programs being the commonly prescribed form of rehabilitation after discharge, mobile health technology coupled with task-oriented programs can enhance self-management of upper extremity training. In the current study, a rehabilitation system, namely mRehab, was designed that included a smartphone app and 3D-printed household items such as mug, bowl, key, and doorknob embedded with a smartphone. The app interface allowed the user to select rehabilitation activities and receive feedback on the number of activity repetitions completed, time to complete each activity, and quality of movement.

This study aimed to assess the usability, perceived usefulness, and acceptance of the mRehab system by individuals with stroke and identify the challenges experienced by them when using the system remotely in a home-based setting.

A mixed-methods approach was usetable. Overall, most participants indicated that they would like to continue using the mRehab system at home.

Assessing usability in the lived environment over a prolonged duration of time is essential to identify the match between the system and users' needs and preferences. While mRehab was well accepted, further customization is desired for a better fit with the end users.

ClinicalTrials.gov NCT04363944; https//clinicaltrials.gov/ct2/show/NCT04363944.

ClinicalTrials.gov NCT04363944; https//clinicaltrials.gov/ct2/show/NCT04363944.

Recent studies suggest that computerized puzzle games are enjoyable, easy to play, and engage attentional, visuospatial, and executive functions. They may help mediate impairments seen in cognitive decline in addition to being an assessment tool. Eye tracking provides a quantitative and qualitative analysis of gaze, which is highly useful in understanding visual search behavior.

The goal of the research was to test the feasibility of eye tracking during a puzzle game and develop adjunct markers for cognitive performance using eye-tracking metrics.

A desktop version of the Match-3 puzzle game with 15 difficulty levels was developed using Unity 3D (Unity Technologies). The goal of the Match-3 puzzle was to find configurations (target patterns) that could be turned into a row of 3 identical game objects (tiles) by swapping 2 adjacent tiles. Difficulty levels were created by manipulating the puzzle board size (all combinations of width and height from 4 to 8) and the number of unique tiles on the puzzle boaognitive processes. Therefore, eye movement metrics might be used as an adjunct marker for cognitive abilities like executive functions. However, further research is needed to evaluate the potential of the various eye movement metrics in combination with puzzle games as visual search and attentional marker.

Physical activity has shown beneficial effects in the treatment of breast cancer fatigue; nevertheless, a significant portion of patients remain insufficiently physically active after breast cancer. Currently most patients have a smartphone, and therefore mobile health (mHealth) holds the promise of promoting health behavior uptake for many of them.

In this study, we explored representations, levers, and barriers to physical activity and mHealth interventions among inactive breast cancer patients with fatigue.

This was an exploratory, qualitative study including breast cancer patients from a French cancer center. A total of 4 focus groups were conducted with 9 patients; 2 independent groups of patients (groups A and B) were interviewed at 2 consecutive times (sessions 1 to 4), before and after their participation in a 2-week mHealth group experience consisting of (1) a competitive virtual exercise group activity (a fictitious world tour), (2) participation in a daily chat network, and (3) access to physical activity information and world tour classification feedback.