UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation.

Organ segmentation has become a preliminary task for computer-aided intervention, diagnosis, radiation therapy, and critical robotic surgery. Automatic organ segmentation from medical images is a challenging task due to the inconsistent shape and size of different organs. Besides this, low contrast at the edges of organs due to similar types of tissue confuses the network's ability to segment the contour of organs properly. In this paper, we propose a novel convolution neural network based uncertainty-driven boundary-refined segmentation network (UDBRNet) that segments the organs from CT images. The CT images are segmented first and produce multiple segmentation masks from multi-line segmentation decoder. Uncertain regions are identified from multiple masks and the boundaries of the organs are refined based on uncertainty data. Our method achieves remarkable performance, boasting dice accuracies of 0.80, 0.95, 0.92, and 0.94 for Esophagus, Heart, Trachea, and Aorta respectively on the SegThor dataset, and 0.71, 0.89, 0.85, 0.97, and 0.97 for Esophagus, Spinal Cord, Heart, Left-Lung, and Right-Lung respectively on the LCTSC dataset. These results demonstrate the superiority of our uncertainty-driven boundary refinement technique over state-of-the-art segmentation networks such as UNet, Attention UNet, FC-denseNet, BASNet, UNet++, R2UNet, TransUNet, and DS-TransUNet. UDBRNet presents a promising network for more precise organ segmentation, particularly in challenging, uncertain conditions. The source code of our proposed method will be available at https://github.com/riadhassan/UDBRNet.

A Scoping Review on Mobile Health Technology for Assessment and Intervention of Upper Limb Motor Function in Children with Motor Impairments.

Upper limb (UL) motor dysfunctions impact residual movement in hands/shoulders and limit participation in play, sports, and leisure activities. Clinical and laboratory assessments of UL movement can be time-intensive, subjective, and/or require specialized equipment and may not optimally capture a child's motor abilities. The restrictions to in-person research experienced during the COVID-19 pandemic have inspired investigators to design inclusive at-home studies with child participants and their families. Relying on the ubiquity of mobile devices, mobile health (mHealth) applications offer solutions for various clinical and research problems. This scoping review article aimed to aggregate and synthesize existing research that used health technology and mHealth approaches to evaluate and assess the hand function and UL movement in children with UL motor impairment. A scoping review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) model was conducted in March 2023 yielding 25 articles (0.32% of 7891 studies). Assessment characteristics included game or task-based tests (13/25, 52%), primarily for neurological disorders (e.g., autism spectrum disorder [ASD], dystonia, dysgraphia) or children with cerebral palsy (CP). Although several mHealth studies were conducted in the clinical environment (10/25, 40%), studies conducted at home or in nonclinical settings (15/25, 60%) reported acceptable and highly satisfactory to the patients as minimizing the potential risks in participation. Moreover, the remaining barriers to clinical translation included object manipulation on a touch screen, offline data analysis, real-world usability, and age-appropriate application design for the wider population. However, the results emphasize the exploration of mHealth over traditional approaches, enabling user-centered study design, family-oriented methods, and large-scale sampling in future research.

Impact of veteran-led peer mentorship on posttraumatic stress disorder.

Peer mentorship shows promise as a strategy to support veteran mental health. A community-academic partnership involving a veteran-led nonprofit organization and institutions of higher education evaluated a collaboratively developed peer mentor intervention. We assessed posttraumatic stress disorder (PTSD), postdeployment experiences, social functioning, and psychological strengths at baseline, midpoint, and 12-week discharge using the PTSD Checklist for DSM-5 (PCL-5), Deployment Risk and Resilience Inventory-2, Social Adaptation Self-evaluation Scale, and Values in Action Survey. Brief weekly check-in surveys reinforced mentor contact and assessed retention. The sample included 307 veterans who were served by 17 veteran peer mentors. Mixed-effects linear models found a modest effect for PTSD symptom change, with a mean PCL-5 score reduction of 4.04 points, 95% CI [-6.44, -1.64], d = 0.44. More symptomatic veterans showed a larger effect, with average reductions of 9.03 points, 95% CI [-12.11, -5.95], d = 0.77. There were no significant findings for other outcome variables. Compared to younger veterans, those aged 32-57 years were less likely to drop out by 6 weeks, aORs = 0.32-0.26. Week-by-week hazard of drop-out was lower with mentors ≥ 35 years old, aHR = 0.62, 95% CI [0.37, 1.05]. Unadjusted survival differed by mentor military branch, p = .028, but the small mentor sample reduced interpretability. Like many community research efforts, this study lacked a control group, limiting the inferences that can be drawn. Continued study of veteran peer mentorship is important as this modality is often viewed as more tolerable than therapy.

Acceptance of bite presentations and feeding behaviors of 8 to 12-month-old infants: A reflection of typical feeding development.

PURPOSE: The lack of age-appropriate expectations for feeding acceptance patterns in early childhood is a barrier to early and accurate identification of pediatric feeding disorder (PFD). The objective of the study was to describe the process by which typically developing children 8-12 months of age accept or refuse bite presentations and their corresponding feeding behaviors, aiming to establish age-appropriate normative data for feeding acceptance. METHOD: Using cross-sectional methodology, we studied the proportion of bite presentations accepted, the type of feeding behaviors-passive, disruptive, expulsion, feeding concerns- observed at presentation and acceptance or refusal, and the duration between presentation to acceptance or refusal in 63 healthy infants between 8 and 12 months of age. Descriptive statistics and a one-way ANOVA were conducted to compare the effect of age and texture. RESULTS: Findings reveal high levels of bite acceptance of 80 % or > for children across ages, but with lower texture-specific differences. Both passive and disruptive behaviors were present even during acceptance of bites without any expulsion. Feeding concerns showed developmental trends with rapid reduction by 12 months suggesting improvement in oral feeding skills. The duration of acceptance and refusals revealed clear patterns by age and texture with an average of 3 s for acceptance but <1 s for refusal. CONCLUSIONS: This study describes bite acceptance patterns in a cohort of typically developing infants between 8 and 12 months of age by examining the acceptance of bites, frequency and type of feeding behaviors, and duration differences when children accept versus refuse a bite. Findings may be applied in the future to provide more sensitive detection of problematic feeding patterns to aid in the detection of pediatric feeding disorder.

24-Hour warning signs for adolescent suicide attempts.

BACKGROUND: Little is known about when youth may be at greatest risk for attempting suicide, which is critically important information for the parents, caregivers, and professionals who care for youth at risk. This study used adolescent and parent reports, and a case-crossover, within-subject design to identify 24-hour warning signs (WS) for suicide attempts. METHODS: Adolescents (N = 1094, ages 13 to 18) with one or more suicide risk factors were enrolled and invited to complete bi-weekly, 8-10 item text message surveys for 18 months. Adolescents who reported a suicide attempt (survey item) were invited to participate in an interview regarding their thoughts, feelings/emotions, and behaviors/events during the 24-hours prior to their attempt (case period) and a prior 24-hour period (control period). Their parents participated in an interview regarding the adolescents' behaviors/events during these same periods. Adolescent or adolescent and parent interviews were completed for 105 adolescents (81.9% female; 66.7% White, 19.0% Black, 14.3% other). RESULTS: Both parent and adolescent reports of suicidal communications and withdrawal from social and other activities differentiated case and control periods. Adolescent reports also identified feelings (self-hate, emotional pain, rush of feelings, lower levels of rage toward others), cognitions (suicidal rumination, perceived burdensomeness, anger/hostility), and serious conflict with parents as WS in multi-variable models. CONCLUSIONS: This study identified 24-hour WS in the domains of cognitions, feelings, and behaviors/events, providing an evidence base for the dissemination of information about signs of proximal risk for adolescent suicide attempts.

A novel approach for standardizing clinical laboratory categorical test results using machine learning and string distance similarity.

Standardizing clinical laboratory test results is critical for conducting clinical data science research and analysis. However, standardized data processing tools and guidelines are inadequate. In this paper, a novel approach for standardizing categorical test results based on supervised machine learning and the Jaro-Winkler similarity algorithm is proposed. A supervised machine learning model is used in this approach for scalable categorization of the test results into predefined groups or clusters, while Jaro-Winkler similarity is used to map text terms into standard clinical terms within these corresponding groups. The proposed method is applied to 75062 test results from two private hospitals in Bangladesh. The Support Vector Classification algorithm with a linear kernel has a classification accuracy of 98%, which is better than the Random Forest algorithm when categorizing test results. The experiment results show that Jaro-Winkler similarity achieves a remarkable 99.93% success rate in the test result standardization for the majority of groups with manual validation. The proposed method outperforms previous studies that concentrated on standardizing test results using rule-based classifiers on a smaller number of groups and distance similarities such as Cosine similarity or Levenshtein distance. Furthermore, when applied to the publicly available MIMIC-III dataset, our approach also performs excellently. All these findings show that the proposed standardization technique can be very beneficial for clinical big data research, particularly for national clinical research data hubs in low- and middle-income countries.

A Consecutive Series Study of the Frequencies, Intensities, and Natural History of Symptoms Following COVID-19 Infection in Nepali Men.

BACKGROUND: In Nepal, approximately one million individuals, two-thirds men, have tested positive for COVID-19. The recovery picture from this infection is undescribed. METHODS: At one major testing institution in Kathmandu, we attempted to contact men three-four months following documentation of a positive PCR Covid test. If the men contacted consented and reported that they had not completely recovered from their Covid infection, we then sought their answers about the presence and intensities of 23 symptoms. RESULTS: Of 2043 consecutive test-positive men, we successfully contacted 1254 men/or family members. 14 men had died before our calls, and two reported having cancer or tuberculosis, providing 1238 individuals. 318 (25.7%) reported that they were unrecovered and 311 of these men were successfully interviewed. At a median of 3.5 months from diagnosis, 216 (17.4%) men reported fatigue, 153 (12.4%) pain, 134 (10.8%) difficulty remembering, 133 (10.7%) reduced physical activity, 114 (9.2%) shortness of breath, and 114 (9.2%) poor sleep. By 6 and 9 months, 108 (8.7%) and 55 (4.4%) of men respectively were still unrecovered. CONCLUSIONS: In this PCR Covid test-positive series of symptomatic men, recovery was significantly prolonged compared with other viral illnesses.

A Survey of Conversational Agents and Their Applications for Self-Management of Chronic Conditions.

Conversational agents have gained their ground in our daily life and various domains including healthcare. Chronic condition self-management is one of the promising healthcare areas in which conversational agents demonstrate significant potential to contribute to alleviating healthcare burdens from chronic conditions. This survey paper introduces and outlines types of conversational agents, their generic architecture and workflow, the implemented technologies, and their application to chronic condition self-management.

Unraveling a blockchain-based framework towards patient empowerment: A scoping review envisioning future smart health technologies.

The COVID-19 pandemic shows us how crucial patient empowerment can be in the healthcare ecosystem. Now, we know that scientific advancement, technology integration, and patient empowerment need to be orchestrated to realize future smart health technologies. In that effort, this paper unravels the Good (advantages), Bad (challenges/limitations), and Ugly (lacking patient empowerment) of the blockchain technology integration in the Electronic Health Record (EHR) paradigm in the existing healthcare landscape. Our study addresses four methodically-tailored and patient-centric Research Questions, primarily examining 138 relevant scientific papers. This scoping review also explores how the pervasiveness of blockchain technology can help to empower patients in terms of access, awareness, and control. Finally, this scoping review leverages the insights gleaned from this study and contributes to the body of knowledge by proposing a patient-centric blockchain-based framework. This work will envision orchestrating three essential elements with harmony: scientific advancement (Healthcare and EHR), technology integration (Blockchain Technology), and patient empowerment (access, awareness, and control).

An Artificial Intelligence-Based Smartphone App for Assessing the Risk of Opioid Misuse in Working Populations Using Synthetic Data: Pilot Development Study.

BACKGROUND: Opioid use disorder (OUD) is an addiction crisis in the United States. As recent as 2019, more than 10 million people have misused or abused prescription opioids, making OUD one of the leading causes of accidental death in the United States. Workforces that are physically demanding and laborious in the transportation, construction and extraction, and health care industries are prime targets for OUD due to high-risk occupational activities. Because of this high prevalence of OUD among working populations in the United States, elevated workers' compensation and health insurance costs, absenteeism, and declined productivity in workplaces have been reported. OBJECTIVE: With the emergence of new smartphone technologies, health interventions can be widely used outside clinical settings via mobile health tools. The major objective of our pilot study was to develop a smartphone app that can track work-related risk factors leading to OUD with a specific focus on high-risk occupational groups. We used synthetic data analyzed by applying a machine learning algorithm to accomplish our objective. METHODS: To make the OUD assessment process more convenient and to motivate potential patients with OUD, we developed a smartphone-based app through a step-by-step process. First, an extensive literature survey was conducted to list a set of critical risk assessment questions that can capture high-risk behaviors leading to OUD. Next, a review panel short-listed 15 questions after careful evaluation with specific emphasis on physically demanding workforces-9 questions had two, 5 questions had five, and 1 question had three response options. Instead of human participant data, synthetic data were used as user responses. Finally, an artificial intelligence algorithm, naive Bayes, was used to predict the OUD risk, trained with the synthetic data collected. RESULTS: The smartphone app we have developed is functional as tested with synthetic data. Using the naive Bayes algorithm on collected synthetic data, we successfully predicted the risk of OUD. This would eventually create a platform to test the functionality of the app further using human participant data. CONCLUSIONS: The use of mobile health techniques, such as our mobile app, is highly promising in predicting and offering mitigation plans for disease detection and prevention. Using a naive Bayes algorithm model along with a representational state transfer (REST) application programming interface and cloud-based data encryption storage, respondents can guarantee their privacy and accuracy in estimating their risk. Our app offers a tailored mitigation strategy for specific workforces (eg, transportation and health care workers) that are most impacted by OUD. Despite the limitations of the study, we have developed a robust methodology and believe that our app has the potential to help reduce the opioid crisis.

Multimedia tablet or paper handout to supplement counseling during preterm birth hospitalization: a randomized controlled trial.

BACKGROUND: Parents of premature infants engage in shared decision-making regarding the care of their infant. The process of prenatal counseling typically involves a verbal conversation with a neonatal provider during hospitalization. Support people may not be available, and the pregnant person's memory is impaired by medications, pain, and stress. The American Academy of Pediatrics, American College of Obstetricians and Gynecologists, and the Eunice Kennedy Shriver National Institute of Child Health and Human Development have called for improvements to this process, including the development of educational aids. OBJECTIVE: This study aimed to investigate whether a multimedia tablet would be more effective than a paper handout in supplementing verbal clinician counseling during preterm birth hospitalization. STUDY DESIGN: This was a randomized controlled trial including English-speaking pregnant people aged ≥18 years and hospitalized at 22 to 33 weeks' gestation for preterm birth. Exclusion criteria were known fetal or chromosomal anomaly and delivery before study completion. Pregnant people received either a multimedia tablet or a paper handout before verbal clinician counseling. Preintervention assessment included demographics and State-Trait Anxiety Inventory, and postintervention assessment included the Parent Knowledge of Premature Birth Questionnaire and State-Trait Anxiety Inventory. Continuous variables were analyzed by t-test and categorical variables by Fisher exact test. RESULTS: A total of 122 pregnant people referred for counseling were screened; 76 were randomized, and 59 completed the study. Demographics were similar between groups, except that pregnant people in the handout group were older (mean 32 vs 29 years; P=.03). The multimedia tablet group (n=32) was less likely to report reviewing all the educational material than the paper handout group (n=27) (41% vs 72%; P=.037). Both groups correctly answered a similar number of knowledge items (P=.088). Postintervention state anxiety decreased in both groups (P<.0001), with no difference between groups. Computerized tracking showed that the multimedia group spent a median of 37 minutes reviewing the tablet. CONCLUSION: Contrary to our hypothesis, a paper handout and multimedia tablet were equally effective in the labor unit for supplementing verbal preterm birth counseling, and both decreased parental anxiety.

Data augmentation strategies for EEG-based motor imagery decoding.

The wide use of motor imagery as a paradigm for brain-computer interfacing (BCI) points to its characteristic ability to generate discriminatory signals for communication and control. In recent times, deep learning techniques have increasingly been explored, in motor imagery decoding. While deep learning techniques are promising, a major challenge limiting their wide adoption is the amount of data available for decoding. To combat this challenge, data augmentation can be performed, to enhance decoding performance. In this study, we performed data augmentation by synthesizing motor imagery (MI) electroencephalography (EEG) trials, following six approaches. Data generated using these methods were evaluated based on four criteria, namely - the accuracy of prediction, the Frechet Inception distance (FID), the t-distributed Stochastic Neighbour Embedding (t-SNE) plots and topographic head plots. We show, based on these, that the synthesized data exhibit similar characteristics with real data, gaining up to 3% and 12% increases in mean accuracies across two public datasets. Finally, we believe these approaches should be utilized in applying deep learning techniques, as they not only have the potential to improve prediction performances, but also to save time spent on subject data collection.

Control of a Wheelchair-Mounted 6DOF Assistive Robot With Chin and Finger Joysticks.

Throughout the last decade, many assistive robots for people with disabilities have been developed; however, researchers have not fully utilized these robotic technologies to entirely create independent living conditions for people with disabilities, particularly in relation to activities of daily living (ADLs). An assistive system can help satisfy the demands of regular ADLs for people with disabilities. With an increasing shortage of caregivers and a growing number of individuals with impairments and the elderly, assistive robots can help meet future healthcare demands. One of the critical aspects of designing these assistive devices is to improve functional independence while providing an excellent human-machine interface. People with limited upper limb function due to stroke, spinal cord injury, cerebral palsy, amyotrophic lateral sclerosis, and other conditions find the controls of assistive devices such as power wheelchairs difficult to use. Thus, the objective of this research was to design a multimodal control method for robotic self-assistance that could assist individuals with disabilities in performing self-care tasks on a daily basis. In this research, a control framework for two interchangeable operating modes with a finger joystick and a chin joystick is developed where joysticks seamlessly control a wheelchair and a wheelchair-mounted robotic arm. Custom circuitry was developed to complete the control architecture. A user study was conducted to test the robotic system. Ten healthy individuals agreed to perform three tasks using both (chin and finger) joysticks for a total of six tasks with 10 repetitions each. The control method has been tested rigorously, maneuvering the robot at different velocities and under varying payload (1-3.5 lb) conditions. The absolute position accuracy was experimentally found to be approximately 5 mm. The round-trip delay we observed between the commands while controlling the xArm was 4 ms. Tests performed showed that the proposed control system allowed individuals to perform some ADLs such as picking up and placing items with a completion time of less than 1 min for each task and 100% success.

Development of a Noninvasive Blood Glucose Monitoring System Prototype: Pilot Study.

BACKGROUND: Diabetes mellitus is a severe disease characterized by high blood glucose levels resulting from dysregulation of the hormone insulin. Diabetes is managed through physical activity and dietary modification and requires careful monitoring of blood glucose concentration. Blood glucose concentration is typically monitored throughout the day by analyzing a sample of blood drawn from a finger prick using a commercially available glucometer. However, this process is invasive and painful, and leads to a risk of infection. Therefore, there is an urgent need for noninvasive, inexpensive, novel platforms for continuous blood sugar monitoring. OBJECTIVE: Our study aimed to describe a pilot test to test the accuracy of a noninvasive glucose monitoring prototype that uses laser technology based on near-infrared spectroscopy. METHODS: Our system is based on Raspberry Pi, a portable camera (Raspberry Pi camera), and a visible light laser. The Raspberry Pi camera captures a set of images when a visible light laser passes through skin tissue. The glucose concentration is estimated by an artificial neural network model using the absorption and scattering of light in the skin tissue. This prototype was developed using TensorFlow, Keras, and Python code. A pilot study was run with 8 volunteers that used the prototype on their fingers and ears. Blood glucose values obtained by the prototype were compared with commercially available glucometers to estimate accuracy. RESULTS: When using images from the finger, the accuracy of the prototype is 79%. Taken from the ear, the accuracy is attenuated to 62%. Though the current data set is limited, these results are encouraging. However, three main limitations need to be addressed in future studies of the prototype: (1) increase the size of the database to improve the robustness of the artificial neural network model; (2) analyze the impact of external factors such as skin color, skin thickness, and ambient temperature in the current prototype; and (3) improve the prototype enclosure to make it suitable for easy finger and ear placement. CONCLUSIONS: Our pilot study demonstrates that blood glucose concentration can be estimated using a small hardware prototype that uses infrared images of human tissue. Although more studies need to be conducted to overcome limitations, this pilot study shows that an affordable device can be used to avoid the use of blood and multiple finger pricks for blood glucose monitoring in the diabetic population.

Longitudinal Assessment of Acute Concussion Outcomes Through SMS Text (ConText Study).

OBJECTIVE: Mild traumatic brain injury (mTBI), or concussion, is a common health problem that has seen a recent increase in US adolescents. This study uses SMS text messaging (a mobile health [mHealth] tool) to report patient symptoms. We aim to better characterize mTBI recovery and hypothesize that this mHealth tool will have high retention rates and correlate with a conventional means of assessing symptoms, the Post-Concussion Symptom Inventory (PCSI). METHODS: A prospective observational cohort pilot study. Thirty-one pediatric patients with acute mTBI were recruited to characterize their injury and report their symptoms via text messaging. Patients reported symptoms once every 3 days for the first 21 days, then once a week for 6 weeks. RESULTS: There was a strong and positive correlation between the PCSI and the mHealth tool (rs = 0.875, P < 0.000, n = 22). Retention was 74% until symptom resolution and 42% until study completion. Patients with balance deficits had a significantly higher somatization score than those with normal balance (6.53 ± 3.25 vs 2.56 ± 2.30, t(22) = 3.211, P < 0.01). CONCLUSIONS: This pilot study demonstrates that this tool is a valid and easy-to-use method of reporting pediatric mTBI symptoms-it replicates and identifies novel findings. Our results suggest that there may be a relationship between balance and the manifestation of somatic symptoms. Retention rates were lower than predicted, indicating that text messaging may not be the ideal format in this population. Text messaging may still have other applications for short-term communication/symptom measurement.

Feasibility and Acceptability of a mHealth Self-Management Intervention for Pediatric Transplant Families.

Families of pediatric solid organ transplant recipients need ongoing education and support in the first 30 days following hospital discharge for the transplantation. The purpose of this report is to describe the feasibility, acceptability, and preliminary efficacy of a mHealth family-self management intervention, (myFAMI), designed to improve post-discharge outcomes of coping, family quality of life, self-efficacy, family self-management, and utilization of health care resources. We enrolled 46 primary family members. myFAMI was feasible and acceptable; 81% (n=17/21) of family members completed the app at least 24/30 days (goal 80% completion rate). Family members generated 134 trigger alerts and received a nurse response within the goal timeframe of < 2 h 99% of the time. Although there were no significant differences between groups, primary outcomes were in the expected direction. The intervention was well received and is feasible for future post-discharge interventions for families of children who receive an organ transplant.

An mHealth App-Based Self-management Intervention for Family Members of Pediatric Transplant Recipients (myFAMI): Framework Design and Development Study.

BACKGROUND: Solid-organ transplantation is the treatment of choice for children with end-stage organ failure. Ongoing recovery and medical management at home after transplant are important for recovery and transition to daily life. Smartphones are widely used and hold the potential for aiding in the establishment of mobile health (mHealth) protocols. Health care providers, nurses, and computer scientists collaboratively designed and developed mHealth family self-management intervention (myFAMI), a smartphone-based intervention app to promote a family self-management intervention for pediatric transplant patients' families. OBJECTIVE: This paper presents outcomes of the design stages and development actions of the myFAMI app framework, along with key challenges, limitations, and strengths. METHODS: The myFAMI app framework is built upon a theory-based intervention for pediatric transplant patients, with aid from the action research (AR) methodology. Based on initially defined design motivation, the team of researchers collaboratively explored 4 research stages (research discussions, feedback and motivations, alpha testing, and deployment and release improvements) and developed features required for successful inauguration of the app in the real-world setting. RESULTS: Deriving from app users and their functionalities, the myFAMI app framework is built with 2 primary components: the web app (for nurses' and superadmin usage) and the smartphone app (for participant/family member usage). The web app stores survey responses and triggers alerts to nurses, when required, based on the family members' response. The smartphone app presents the notifications sent from the server to the participants and captures survey responses. Both the web app and the smartphone app were built upon industry-standard software development frameworks and demonstrate great performance when deployed and used by study participants. CONCLUSIONS: The paper summarizes a successful and efficient mHealth app-building process using a theory-based intervention in nursing and the AR methodology in computer science. Focusing on factors to improve efficiency enabled easy navigation of the app and collection of data. This work lays the foundation for researchers to carefully integrate necessary information (from the literature or experienced clinicians) to provide a robust and efficient solution and evaluate the acceptability, utility, and usability for similar studies in the future. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1002/nur.22010.

Towards Developing a Voice-activated Self-monitoring Application (VoiS) for Adults with Diabetes and Hypertension.

The integration of motivational strategies and self-management theory with mHealth tools is a promising approach to changing the behavior of patients with chronic disease. In this manuscript, we describe the development and current architecture of a prototype voice-activated self-monitoring application (VoiS) which is based on these theories. Unlike prior mHealth applications which require textual input, VoiS app relies on the more convenient and adaptable approach of asking users to verbally input markers of diabetes and hypertension control through a smart speaker. The VoiS app can provide real-time feedback based on these markers; thus, it has the potential to serve as a remote, regular, source of feedback to support behavior change. To enhance the usability and acceptability of the VoiS application, we will ask a diverse group of patients to use it in real-world settings and provide feedback on their experience. We will use this feedback to optimize tool performance, so that it can provide patients with an improved understanding of their chronic conditions. The VoiS app can also facilitate remote sharing of chronic disease control with healthcare providers, which can improve clinical efficacy and reduce the urgency and frequency of clinical care encounters. Because the VoiS app will be configured for use with multiple platforms, it will be more robust than existing systems with respect to user accessibility and acceptability.

A Novel Framework for Mixed Reality-Based Control of Collaborative Robot: Development Study.

BACKGROUND: Applications of robotics in daily life are becoming essential by creating new possibilities in different fields, especially in the collaborative environment. The potentials of collaborative robots are tremendous as they can work in the same workspace as humans. A framework employing a top-notch technology for collaborative robots will surely be worthwhile for further research. OBJECTIVE: This study aims to present the development of a novel framework for the collaborative robot using mixed reality. METHODS: The framework uses Unity and Unity Hub as a cross-platform gaming engine and project management tool to design the mixed reality interface and digital twin. It also uses the Windows Mixed Reality platform to show digital materials on holographic display and the Azure mixed reality services to capture and expose digital information. Eventually, it uses a holographic device (HoloLens 2) to execute the mixed reality-based collaborative system. RESULTS: A thorough experiment was conducted to validate the novel framework for mixed reality-based control of a collaborative robot. This framework was successfully applied to implement a collaborative system using a 5-degree of freedom robot (xArm-5) in a mixed reality environment. The framework was stable and worked smoothly throughout the collaborative session. Due to the distributed nature of cloud applications, there is a negligible latency between giving a command and the execution of the physical collaborative robot. CONCLUSIONS: Opportunities for collaborative robots in telerehabilitation and teleoperation are vital as in any other field. The proposed framework was successfully applied in a collaborative session, and it can also be applied in other similar potential applications for robust and more promising performance.