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creating suitable technology. Adding to our previous knowledge of noncomputerized cognitive stimulation therapy, the release of the iCST app will make this psychosocial intervention accessible to more users worldwide.

This study proves that an agile approach toward technology development involving all relevant stakeholders is effective in creating suitable technology. Adding to our previous knowledge of noncomputerized cognitive stimulation therapy, the release of the iCST app will make this psychosocial intervention accessible to more users worldwide.

Engagement with physical activity mobile apps has been reported to be a core precondition for their effectiveness in digital behavior change interventions. However, to date, little attention has been paid to understanding the perspectives, needs, expectations, and experiences of potential users with physical activity mobile apps.

The aim of this study was to investigate the features that are judged to be important for engagement with a physical activity mobile app and the reasons for their importance.

A qualitative focus-group methodology with elements of co-design was adopted in this study. Participants reporting sedentary lifestyles and willingness to improve their physical activity behavior through mobile technology were recruited. The focus group sessions consisted of 13 participants (8 men and 5 women, mean [SD] age 41.9 [7.1] years). Two researchers conducted the data analysis independently by using the inductive thematic approach.

Four main themes emerged in relation to the research question anwith physical activity mobile apps. The emerged findings may orient future research and interventions aiming to foster engagement of potential users with physical activity apps.

The COVID-19 pandemic has become a public health emergency of international concern; it has not only threatened people's physical health but has also affected their mental health and psychological well-being. It is necessary to develop and offer strategies to reduce the psychological impact of the outbreak and promote adaptive coping.

This study protocol aims to describe a self-administered web-based intervention (Mental Health COVID-19) based on the principles of positive psychology supported by elements of cognitive behavioral therapy and behavioral activation therapy to reduce the symptoms of anxiety and depression and increase positive emotions and sleep quality during and after the COVID-19 outbreak through a telepsychology system.

A randomized controlled clinical superiority trial with two independent groups will be performed, with intrasubject measures at four evaluation periods pretest, posttest, 3-month follow-up, and 6-month follow-up. Participants will be randomly assigned to one of two groupith the COVID-19 pandemic.

ClinicalTrials.gov NCT04468893; https//clinicaltrials.gov/ct2/show/NCT04468893.

DERR1-10.2196/23117.

DERR1-10.2196/23117.

Despite the large impact that dietary habits have in the management of diabetes, few tools for supporting healthy eating habits are available for persons with diabetes.

The aim of this randomized clinical trial is to evaluate the effect of a 12-week, mobile health (mHealth), app-based intervention promoting healthy eating habits among patients with type 2 diabetes.

The HAPPY (Healthy eating using APP technologY) trial is a randomized clinical trial with two arms aiming to include 200 patients, 18 years of age or older, with type 2 diabetes. Both women and men are eligible for inclusion. Study participants are randomized 11 to an intervention group, where they are instructed to use a smartphone app promoting healthy eating, or to a control group, where they receive standard primary care only, for a period of 12 weeks. Each week a new topic (eg, vegetable intake) is introduced via the app. After an introduction text, the user is given a topic-related activity to perform (eg, eat one additional serving of t long-term adherence to healthier eating habits. mHealth-based approaches allow for real-time interaction and the delivery of an intervention at any time. Further, focusing on overall diet allows the user to apply new knowledge to current eating patterns, creating an individualized approach. In this study, we evaluate the effect of using a new smartphone app promoting healthy eating habits on dietary intake, clinical markers, and lifestyle factors among patients with type 2 diabetes.

ClinicalTrials.gov NCT03784612; https//clinicaltrials.gov/ct2/show/NCT03784612.

DERR1-10.2196/24422.

DERR1-10.2196/24422.

CT-QFR is a novel coronary computed tomography angiography (CTA) based method for on-site evaluation of patients with suspected obstructive coronary artery disease (CAD). We compared the diagnostic performance of CT-QFR with myocardial perfusion scintigraphy (MPS) and cardiovascular magnetic resonance (CMR) as second-line tests in patients with suspected obstructive CAD after coronary CTA.

Paired analysis of CT-QFR and MPS or CMR, with an invasive FFR-based classification as reference standard. Symptomatic patients with >50% diameter stenosis on coronary CTA were randomized to MPS or CMR and referred for invasive coronary angiography. The rate of coronary CTA not feasible for CT-QFR analysis was 17%. Paired patient-level data were available for 118 patients in the MPS group and 113 in the CMR group, respectively. Patient-level diagnostic accuracy was better for CT-QFR than for both MPS ((82.2% (95%CI 75.2-89.2) vs. 70.3% (95%CI 62.0-78.7), p=0.029) and CMR ((77.0% (95%CI 69.1-84.9) vs. 65.5% (95%CI 56.6-74.4), p=0.047). Following a positive coronary CTA and with the intention-to diagnose, CT-QFR, CMR and MPS were equally suitable as rule-in and rule-out modalities.

The diagnostic performance of CT-QFR as second-line test was at least similar to MPS and CMR for the evaluation of obstructive coronary artery disease in symptomatic patients presenting with ≥50% diameter stenosis on coronary CTA.

The diagnostic performance of CT-QFR as second-line test was at least similar to MPS and CMR for the evaluation of obstructive coronary artery disease in symptomatic patients presenting with ≥50% diameter stenosis on coronary CTA.Patient satisfaction is a key performance indicator of patient-centered care and hospital reimbursement. To discover the major factors that affect patient experiences is considered as an effective way to formulate corrective actions. A patient during his/her healthcare journey interacts with multiple health professionals across different service units. The health-related data generated at each step of the journey is a valuable resource for extracting actionable insights. In particular, self-reported satisfaction survey and the associated patient electronic health records play an important role in the hospital-patient interaction analysis. In this paper, we propose an interpretable machine learning framework to formulate the patient satisfaction problem as a supervised learning task and utilize a mixed-integer programming model to identify the most influential factors. Santacruzamate A inhibitor The proposed framework transforms heterogeneous data into human-understandable features and integrates feature transformation, variable selection, and coefficient learning into the optimization process. Therefore, it can achieve desirable model performance while maintaining excellent model interpretability, which paves the way for successful real-world applications.The vulnerability to the electrode shift was one of the key barriers to the wide application of pattern recognition-based (PR-based) myoelectric control systems outside the controlled laboratory conditions. To overcome this challenge, a novel framework named position identification (PI) was proposed. In the PI framework, an anchor gesture performed by the user was first analyzed to identify the current electrode position from a pool of potential electrode shift positions. Next, the classifier calibrated by the data of the identified position would be selected for following myoelectric control tasks. The results of the amputee and able-bodied participants both demonstrated that the differential filter combined with majority voting improved the PI accuracy. With only one second contraction of the chosen anchor gesture (hand close), the subsequent PR-based myoelectric control performance was fully restored from eight different electrode shift scenarios, with 1 cm in either or both perpendicular and parallel directions. The classification accuracies with PI framework were not significant before and after the shift ( 0.001). The advantage of restoring performance fully in just one second made it a practical solution to improve the robustness of PR-based myoelectric control systems in a wide range of real-world applications.System identification models relating forearm electromyogram (EMG) signals to phantom wrist radial-ulnar deviation force, pronation-supination moment and/or hand open-close force (EMG-force) are hampered by lack of supervised force/moment output signals in limb-absent subjects. In 12 able-bodied and 7 unilateral transradial limb-absent subjects, we studied three alternative supervised output sources in one degree of freedom (DoF) and 2-DoF target tracking tasks (1) bilateral tracking with force feedback from the contralateral side (non-dominant for able-bodied/ sound for limb-absent subjects) with the contralateral force as the output, (2) bilateral tracking with force feedback from the contralateral side with the target as the output, and (3) dominant/limb-absent side unilateral target tracking without feedback and the target used as the output. "Best-case" EMG-force errors averaged ~ 10% of maximum voluntary contraction (MVC) when able-bodied subjects' dominant limb produced unilateral force/moment with feedback. When either bilateral tracking source was used as the model output, statistically larger errors of 12-16 %MVC resulted. The no-feedback alternative produced errors of 25-30 %MVC, which was nearly half the tested force range of ± 30 %MVC. Therefore, the no-feedback model output was not acceptable. We found little performance variation between DoFs. Many subjects struggled to perform 2-DoF target tracking.Recent advances in robotics, neuroscience, and signal processing make it possible to operate a robot through electroencephalography (EEG)-based brain-computer interface (BCI). Although some successful attempts have been made in recent years, the practicality of the entire system still has much room for improvement. The present study designed and realized a robotic arm control system by combing augmented reality (AR), computer vision, and steady-state visual evoked potential (SSVEP)-BCI. AR environment was implemented by a Microsoft HoloLens. Flickering stimuli for eliciting SSVEPs were presented on the HoloLens, which allowed users to see both the robotic arm and the user interface of the BCI. Thus users did not need to switch attention between the visual stimulator and the robotic arm. A four-command SSVEP-BCI was built for users to choose the specific object to be operated by the robotic arm. Once an object was selected, the computer vision would provide the location and color of the object in the workspace.