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Objective: The NIH All of Us Research Program aims to advance personalized medicine by not only linking patient records, surveys, and genomic data but also engaging with participants, particularly from groups traditionally underrepresented in biomedical research (UBR). This study details how the dialogue between scientists and community members, including many from communities of color, shaped local research priorities., Materials and Methods: We recruited area quantitative, basic, and clinical scientists as well as community members from our Community and Participant Advisory Boards with a predetermined interest in All of Us research as members of a Special Interest Group (SIG). An expert community engagement scientist facilitated 6 SIG meetings over the year, explicitly fostering openness and flexibility during conversations. We qualitatively analyzed discussions using a social movement framework tailored for community-based participatory research (CBPR) mobilization., Results: The SIG evolved through CBPR stages of emergence, coalescence, momentum, and maintenance/integration. Researchers prioritized community needs above personal academic interests while community members kept discussions focused on tangible return of value to communities. One key outcome includes SIG-driven shifts in programmatic and research priorities of the All of Us Research Program in Southeastern Wisconsin. One major challenge was building equitable conversations that balanced scientific rigor and community understanding., Discussion: Our approach allowed for a rich dialogue to emerge. Points of connection and disconnection between community members and scientists offered important guidance for emerging areas of genomic inquiry., Conclusion: Our study presents a robust foundation for future efforts to engage diverse communities in CBPR, particularly on healthcare concerns affecting UBR communities., (Published by Oxford University Press on behalf of the American Medical Informatics Association 2024.)

•Black women receiving the smartphone P3 program had more preterm birth knowledge.•Black women receiving the P3 program were more prepared for healthcare decisions.•Black women receiving the P3 program talked more with partners about preterm birth.•Smartphone preterm birth education may overcome clinic-based education barriers., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mir A Basir reports financial support was provided by National Institutes of Health Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier B.V.)

Objective: Evaluate the effect of fathers' participation in the Preemie Prep for Parents (P3) program on maternal learning and fathers' preterm birth knowledge., Methods: Mothers with preterm birth predisposing medical condition(s) enrolled with or without the baby's father and were randomized to the P3 intervention (text-messages linking to animated videos) or control (patient education webpages). Parent Prematurity Knowledge Questionnaire assessed knowledge, including unmarried fathers' legal neonatal decision-making ability., Results: 104 mothers reported living with the baby's father; 50 participated with the father and 54 participated alone. In the P3 group, mothers participating with the father (n=33) had greater knowledge than mothers participating alone (n=21), 85% correct responses vs. 76%, p =0.033. However, there was no difference in knowledge among the control mothers, 67% vs. 60%, p =0.068. P3 fathers (n=33) knowledge scores were not different than control fathers (n=17), 77% vs. 68%, p =0.054. Parents who viewed the video on fathers' rights (n=58) were more likely than those who did not (n=96) to know unmarried fathers' legal inability to decide neonatal treatments, 84% vs. 41%, p <0.001., Conclusions: Among opposite-sex cohabitating couples, fathers' participation in the P3 program enhanced maternal learning., Practice Implications: The P3 program's potential to educate fathers may benefit high-risk pregnancies.

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., (© 2024 International Society for Traumatic Stress Studies.)

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., Competing Interests: NO authors have competing interests., (Copyright: © 2024 Hassan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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.

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.

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., Competing Interests: Declaration of competing interest None declared., (Copyright © 2024. Published by Elsevier Inc.)

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

Single nucleotide polymorphisms (SNPs) in the TUBB1 (β-tubulin) gene have been implicated as the primary cause of macro thrombocytopenia. Therefore it is essential to identify the potential SNPs which are harmful to cause diseases such as macro thrombocytopenia. The impact caused by these variants on β-tubulin is twofold, both structural and functional. Multiple in-silico tools were used to scrutinise the most deleterious nsSNPs (non-synonymous SNPs) via sequence and structure-based approaches. Further, the β-tubulin protein model incorporating identified mutants was subjected to MD (molecular dynamic) simulations to analyse the impact on protein structure. A total of 2974 SNPs of TUBB1 were retrieved from various sources, and 32 nsSNPs were identified. By screening through sequence-based technique, 13 variants were detected as deleterious and further structure-based filtration was carried out to find thermally destabilising variants. Finally, three variants have been detected as highly destabilising by the mCSM server and chosen for the MD study. All three variants are present in the N-terminal, Intermediate, and C-terminal regions, breaking the spatial arrangement required for microtubule assembly. The spatial arrangement of these variants is in deviation with respect to WT (wild type) β-tubulin. The protein model was subjected to a simulation period of 100 ns. The FEL analysis revealed multiple clusters with minor populations indicating the unstable conformation adapted by the β-tubulin. The normal mode vector analysis exhibited high-intensity flexible motions at the C-terminal end, responsible for binding with MAPs (microtubule-associated proteins), an essential region in microtubule assembly. All these results reveal that the SNP's predicted eventually influence the spatial arrangement of β-tubulin, which would disturb the stacking arrangement of αβ tubulin dimer in microtubule assembly. The present study may set a path to cure the diseases like macro thrombocytopenia.Communicated by Ramaswamy H. Sarma.

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.

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)., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 Elsevier Inc. All rights reserved.)

Importance: Preterm birth is a leading cause of infant mortality and child morbidity. Preterm birth is not always unexpected, yet standard prenatal care does not offer anticipatory education to parents at risk of delivering preterm, which leaves parents unprepared to make health care choices during the pregnancy that can improve survival and decrease morbidity in case of preterm birth., Objective: To evaluate the effect of the Preemie Prep for Parents (P3) program on maternal knowledge of preterm birth, preparation for decision-making, and anxiety., Design, Setting, and Participants: Recruitment for this randomized clinical trial conducted at a US academic medical center took place from February 3, 2020, to April 12, 2021. A total of 120 pregnant persons with a risk factor for preterm birth were enrolled between 16 and 21 weeks' gestational age and followed up through pregnancy completion., Intervention: Starting at 18 weeks' gestational age, P3 program participants received links delivered via text message to 51 gestational age-specific short animated videos. Control participants received links to patient education webpages from the American College of Obstetricians and Gynecologists., Main Outcomes and Measures: At 25 weeks' gestation, scores on the Parent Prematurity Knowledge Questionnaire (scored as percent correct), Preparation for Decision Making Scale (scored 0-100), and Patient-Reported Outcomes Measurement Information System (PROMIS) Anxiety computerized adaptive test. Analysis was based on an intention to treat., Results: A total of 120 pregnant participants (mean [SD] age, 32.5 [4.9] years) were included in the study; 60 participants were randomized to each group. Participants in the P3 group scored higher than those in the control group on knowledge of long-term outcomes at 25 weeks (88.5% vs 73.2%; estimated difference, 15.3 percentage points; 95% CI, 8.3-22.5 percentage points; P < .001). Participants in the P3 group reported being significantly more prepared than did participants in the control group for neonatal resuscitation decision-making at 25 weeks (Preparation for Decision Making Scale score, 76.0 vs 52.3; difference, 23.7; 95% CI, 14.1-33.2). There was no difference between the P3 group and the control group in anxiety at 25 weeks (mean [SE] PROMIS Anxiety scores, 53.8 [1.1] vs 54.0 [1.1]; difference, -0.1; 95% CI, -3.2 to 2.9)., Conclusions and Relevance: In this randomized clinical trial, pregnant persons randomly assigned to the P3 program had more knowledge of core competencies and were more prepared to make decisions that affect maternal and infant health, without experiencing worse anxiety. Mobile antenatal preterm birth education may provide a unique benefit to parents with preterm birth risk factors., Trial Registration: ClinicalTrials.gov Identifier: NCT04093492.

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.

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., (©A B M Rezbaul Islam, Khalid M Khan, Amanda Scarbrough, Mariah Jade Zimpfer, Navya Makkena, Adebola Omogunwa, Sheikh Iqbal Ahamed. Originally published in JMIR Formative Research (https://formative.jmir.org), 30.05.2023.)

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., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The human toll-like receptor ( hTLR ) 4 single nucleotide polymorphisms (SNPs) are interconnected with cancer, multiple genetic disorders and other immune-related diseases. The detrimental effect of SNPs in hTLR 4 with respect to structure and function has not been explored in depth. The present study concatenates the biological consequences of the SNPs along with structural modifications predicted at the hTLR4 gene. A total of 7910 SNPs of hTLR4 were screened, and 21 damage-causing SNPs were identified. Out of 21, seven are present in the extracellular region, of which three were detected as deleterious and the fourth one as moderate. These three mutations are located in a highly conserved region and influence conformational change. The change leads to the widening of the Leucine-rich repeat (LRR) arc to a maximum of 16.9 Å and a minimum of 8.7 Å. Expansion/shortening of LRR arc, never discussed before, would cause loss of myeloid differentiation factor 2 (MD-2) interactions in the interior and diminish lipopolysaccharide (LPS) responses. Similarly, in all mutant structures, the binding region for HMGB1 and LPS is deflating or in an unsupportive conformation. Thus, SNPs affect the regular signaling cascade and might result in human sepsis, genetic disorders, cancer and other immunological related diseases.Communicated by Ramaswamy H. Sarma.

Protecting personal health records is becoming increasingly important as more people use Mobile Health applications (mHealth apps) to improve their health outcomes. These mHealth apps enable consumers to monitor their health-related problems, store, manage, and share health records, medical conditions, treatment, and medication. With the increase of mHealth apps accessibility and usability, it is crucial to create, receive, maintain or transmit protected health information (PHI) on behalf of a covered entity or another business associate. The Health Insurance Portability and Accountability Act (HIPAA) provides guidelines to the app developers so that the apps must be compliant with required and addressable Technical Safeguards. However, most mobile app developers, including mHealth apps are not aware of HIPAA security and privacy regulations. Therefore, a research opportunity has emerged to develop an analytical framework to assist the developer to maintain a secure and HIPAA-compliant source code and raise awareness among consumers about the privacy and security of sensitive and personal health information. We proposed an Android source code analysis framework that evaluates twelve HIPAA Technical Safeguards to check whether a mHealth application is HIPAA compliant or not. The implemented meta-analysis and data-flow analysis algorithms efficiently identify the risk and safety features of mHealth apps that violate HIPAA regulations. Furthermore, we addressed API level checking for secure data communication mandated by recent CMS guidelines between third-party mobile health apps and EHR systems. Experimentally, a web-based tool has been developed for evaluating the efficacy of analysis techniques and algorithms. We have investigated 200 top popular Medical and Health & Fitness category Android apps collected from Google Play Store. We identified from the comparative analysis of the HIPAA rules assessment results that authorization to access sensitive resources, data encryption-decryption, and data transmission security is the most vulnerable features of the investigated apps. We provided recommendations to app developers about the most common mistake made at the time of app development and how to avoid these mistakes to implement secure and HIPAA-compliant apps. The proposed framework enables us to develop an IDE plugin for mHealth app developers and a web-based interface for mHealth app consumers.

Background: Cereblon, an extensively studied multifunctional protein, is a Cullin 4-RING E3 ubiquitin ligase complex component. Cereblon is a well-known target of thalidomide and its derivatives. Cereblon is involved in multiple myeloma cell apoptosis. When ligands such as thalidomide and lenalidomide bind to cereblon, it recognizes various neosubstrates based on the ligand shape and properties. We have identified novel CRBN inhibitors, namely DHFO and its analogs, with structural features that are slightly different from thalidomide but stronger cereblon-binding affinity. We selected indanedione and indanone derivatives from the literature to understand and compare their cereblon-mediated substrate recognition potential., Methods: Computational investigations of possible CRBN inhibitors were investigated by molecular docking with Autodock Vina and DockThor programs. The properties of the compounds' ADME/T and drug-likeness were investigated. A molecular dynamics study was carried out for four selected molecules, and the molecular interactions were analyzed using PCA-based FEL methods. The binding affinity was calculated using the MM/PBSA method., Results: We conducted computational investigations on 68 indanedione and indanone derivatives binding with cereblon. Ten molecules showed better CRBN binding affinity than thalidomide. We studied the drug-likeness properties of the selected ten molecules, and four of the most promising molecules (DHFO, THOH, DIMS, and DTIN) were chosen for molecular dynamics studies. The MM/PBSA calculations showed that the DHFO, already shown to be a 5-LOX/COX2 inhibitor, has the highest binding affinity of - 163.16 kJ/mol with cereblon., Conclusion: The selected CRBN inhibitor DHFO has demonstrated the highest binding affinity with cereblon protein compared to other molecules. Thalidomide and its derivatives have a new substitute in the form of DHFO, which produces an interaction hotspot on the surface of the cereblon. Ease of chemical synthesis, low toxicity, versatile therapeutic options, and pleiotropism of DHFO analogs provide an opportunity for exploring clinical alternatives with versatile therapeutic potential for a new category of indanedione molecules as novel modulators of E3 ubiquitin ligases., Competing Interests: Declaration of Interest The authors declare no financial or personal relationships that are received for this work from any commercial institutions or industry. Conflict of Interest The writers affirm that they have received no compensation from commercial organizations or industries for this research work., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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.

This study compares quality of discharge teaching and care coordination for parents of children with challenging behaviors participating in a nursing implementation project, which used an interactive iPad application, to usual discharge care. Unlike parents in the larger quasi-experimental longitudinal project, parents of children with challenging behaviors receiving the discharge teaching application (n = 14) reported lower mean scores on the quality of discharge teaching scale-delivery subscale ( M  = 8.2, SD  = 3.1) than parents receiving usual care (n = 11) ( M  = 9.6, SD  = 4.7) and lower scores on the Care Transition Measure ( M  = 2.44, SD  = 1.09) than parents receiving usual care ( M  = 3.02, SD  = 0.37), with moderate to large effects (0.554-0.775). The discharge teaching approach was less effective with this subset, suggesting other approaches might be considered for this group of parents. Further study with a larger sample specific to parents of children with challenging behaviors is needed to assess their unique needs and to optimize their discharge experience.

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., (©Maria Valero, Priyanka Pola, Oluwaseyi Falaiye, Katherine H Ingram, Liang Zhao, Hossain Shahriar, Sheikh Iqbal Ahamed. Originally published in JMIR Formative Research (https://formative.jmir.org), 26.08.2022.)

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., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s).)

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., Competing Interests: TL and JP were employed by the Company UFACTORY Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest., (Copyright © 2022 Rulik, Sunny, Sanjuan De Caro, Zarif, Brahmi, Ahamed, Schultz, Wang, Leheng, Longxiang and Rahman.)

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.

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., (©Md Tanzil Shahria, Md Samiul Haque Sunny, Md Ishrak Islam Zarif, Md Mahafuzur Rahaman Khan, Preet Parag Modi, Sheikh Iqbal Ahamed, Mohammad H Rahman. Originally published in JMIR Biomedical Engineering (http://biomsedeng.jmir.org), 17.05.2022.)

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., (©Riddhiman Adib, Dipranjan Das, Sheikh Iqbal Ahamed, Stacee Marie Lerret. Originally published in JMIR Nursing (https://nursing.jmir.org), 04.01.2022.)

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., Competing Interests: Disclosure: The authors declare no conflict of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Building control architecture that balances the assistive manipulation systems with the benefits of direct human control is a crucial challenge of human-robot collaboration. It promises to help people with disabilities more efficiently control wheelchair and wheelchair-mounted robot arms to accomplish activities of daily living., Methods: In this study, our research objective is to design an eye-tracking assistive robot control system capable of providing targeted engagement and motivating individuals with a disability to use the developed method for self-assistance activities of daily living. The graphical user interface is designed and integrated with the developed control architecture to achieve the goal., Results: We evaluated the system by conducting a user study. Ten healthy participants performed five trials of three manipulation tasks using the graphical user interface and the developed control framework. The 100% success rate on task performance demonstrates the effectiveness of our system for individuals with motor impairments to control wheelchair and wheelchair-mounted assistive robotic manipulators., Conclusions: We demonstrated the usability of using this eye-gaze system to control a robotic arm mounted on a wheelchair in activities of daily living for people with disabilities. We found high levels of acceptance with higher ratings in the evaluation of the system with healthy participants., (© 2021. The Author(s).)

Community-wide lockdowns in response to COVID-19 influenced many families, but the developmental cascade for children with autism spectrum disorder (ASD) may be especially detrimental. Our objective was to evaluate behavioral patterns of risk and resilience for children with ASD across parent-report assessments before (from November 2019 to February 2020), during (March 2020 to May 2020), and after (June 2020 to November 2020) an extended COVID-19 lockdown. In 2020, our study Mobile-based care for children with ASD using remote experience sampling method (mCARE) was inactive data collection before COVID-19 emerged as a health crisis in Bangladesh. Here we deployed "Cohort Studies", where we had in total 300 children with ASD (150 test group and 150 control group) to collect behavioral data. Our data collection continued through an extended COVID-19 lockdown and captured parent reports of 30 different behavioral parameters (e.g., self-injurious behaviors, aggression, sleep problems, daily living skills, and communication) across 150 children with ASD (test group). Based on the children's condition, 4-6 behavioral parameters were assessed through the study. A total of 56,290 behavioral data points was collected (an average of 152.19 per week) from parent cell phones using the mCARE platform. Children and their families were exposed to an extended COVID-19 lockdown. The main outcomes used for this study were generated from parent reports child behaviors within the mCARE platform. Behaviors included of child social skills, communication use, problematic behaviors, sensory sensitivities, daily living, and play. COVID-19 lockdowns for children with autism and their families are not universally negative but supports in the areas of "Problematic Behavior" could serve to mitigate future risk., (© 2021. The Author(s).)

Background: Care for children with autism spectrum disorder (ASD) can be challenging for families and medical care systems. This is especially true in low- and- middle-income countries such as Bangladesh. To improve family-practitioner communication and developmental monitoring of children with ASD, mCARE (Mobile-Based Care for Children with Autism Spectrum Disorder Using Remote Experience Sampling Method) was developed. Within this study, mCARE was used to track child milestone achievement and family sociodemographic assets to inform mCARE feasibility/scalability and family asset-informed practitioner recommendations., Objective: The objectives of this paper are threefold. First, it documents how mCARE can be used to monitor child milestone achievement. Second, it demonstrates how advanced machine learning models can inform our understanding of milestone achievement in children with ASD. Third, it describes family/child sociodemographic factors that are associated with earlier milestone achievement in children with ASD (across 5 machine learning models)., Methods: Using mCARE-collected data, this study assessed milestone achievement in 300 children with ASD from Bangladesh. In this study, we used 4 supervised machine learning algorithms (decision tree, logistic regression, K-nearest neighbor [KNN], and artificial neural network [ANN]) and 1 unsupervised machine learning algorithm (K-means clustering) to build models of milestone achievement based on family/child sociodemographic details. For analyses, the sample was randomly divided in half to train the machine learning models and then their accuracy was estimated based on the other half of the sample. Each model was specified for the following milestones: Brushes teeth, Asks to use the toilet, Urinates in the toilet or potty, and Buttons large buttons., Results: This study aimed to find a suitable machine learning algorithm for milestone prediction/achievement for children with ASD using family/child sociodemographic characteristics. For Brushes teeth, the 3 supervised machine learning models met or exceeded an accuracy of 95% with logistic regression, KNN, and ANN as the most robust sociodemographic predictors. For Asks to use toilet, 84.00% accuracy was achieved with the KNN and ANN models. For these models, the family sociodemographic predictors of "family expenditure" and "parents' age" accounted for most of the model variability. The last 2 parameters, Urinates in toilet or potty and Buttons large buttons, had an accuracy of 91.00% and 76.00%, respectively, in ANN. Overall, the ANN had a higher accuracy (above ~80% on average) among the other algorithms for all the parameters. Across the models and milestones, "family expenditure," "family size/type," "living places," and "parent's age and occupation" were the most influential family/child sociodemographic factors., Conclusions: mCARE was successfully deployed in a low- and middle-income country (ie, Bangladesh), providing parents and care practitioners a mechanism to share detailed information on child milestones achievement. Using advanced modeling techniques this study demonstrates how family/child sociodemographic elements can inform child milestone achievement. Specifically, families with fewer sociodemographic resources reported later milestone attainment. Developmental science theories highlight how family/systems can directly influence child development and this study provides a clear link between family resources and child developmental progress. Clinical implications for this work could include supporting the larger family system to improve child milestone achievement., (©Munirul M Haque, Masud Rabbani, Dipranjan Das Dipal, Md Ishrak Islam Zarif, Anik Iqbal, Amy Schwichtenberg, Naveen Bansal, Tanjir Rashid Soron, Syed Ishtiaque Ahmed, Sheikh Iqbal Ahamed. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 08.06.2021.)

Two billion people are affected by hemoglobin (Hgb) related diseases. Usual clinical assessments of Hgb are conducted by analyzing venipuncture-obtained blood samples in laboratories. A non-invasive, cheap, point-of-care and accurate Hgb test is needed everywhere. Our group has developed a non-invasive Hgb measurement system using 10-second Smartphone videos of the index fingertips. Custom hardware sets were used to illuminate the fingers. We tested four lighting conditions with wavelengths in the near-infrared spectrum suggested by the absorption properties of two primary components of blood-oxygenated Hgb and plasma. We found a strong linear correlation between our measured and laboratory-measured Hgb levels in 167 patients with a mean absolute percentage error (MAPE) of 5%. In our initial analysis, critical tasks were performed manually. Now, using the same data, we have automated or modified all the steps. For all, male, and female subjects we found a MAPE of 6.43%, 5.34%, and 4.85 and mean squared error (MSE) of 0.84, 0.5, and 0.49 respectively. The new analyses however, have suggested inexplicable inconsistencies in our results, which we attribute to laboratory measurement errors reflected in a non-normative distribution of Hgb levels in our studied patients, as well as excess noise in the specific signals we measured in the videos. Based on these encouraging results, and the promise of greater accuracy with our revised hardware and software tools, we now propose a rigorous validation study to demonstrate that this approach to hemoglobin measurement is appropriate for general clinical application.

The current SARS-CoV-2, better know as COVID-19, has emerged as a serious pandemic with life-threatening clinical manifestations and a high mortality rate. One of the major complications of this disease is the rapid and dangerous pulmonary deterioration that can lead to critical pneumonia conditions, resulting in death. The current healthcare system around the world faces the potential problem of lacking resources to assist a large number of patients at the same time; then, the non-critical patients are mostly referred to perform self-isolation/quarantine at home. This pandemic has placed new demands on the health systems world , asking for novel, rapid and secure ways to monitor patients in order to detect and quickly report patient's symptoms to the healthcare provider, even if they are not in the hospital. While tremendous efforts have been done to develop technologies to detect the virus, create the vaccine, and stop the spread of the disease, it is also important to develop IoT technologies that can help track and monitor diagnosed COVID-19 patients from their homes. In this paper, we explore the possibility of monitoring respiration rates (RR) of COVID-19 patients using a widely-available technology at home - WiFi. Using the at-home WiFi signals, we propose Wi-COVID, a non-invasive and non-wearable technology to monitor the patient and track RR for the healthcare provider. We first introduce the currently available applications that can be done using WiFi signals. Then, we propose the framework scheme for an end-to-end non-invasive monitoring platform of the COVID-19 patients using WiFi. Finally, we present some preliminary results of the proposed framework. We envision the proposed platform as a life-changing technology that leverages WiFi technology as a non-wearable and non-invasive way to monitor COVID-19 patients at home., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 Published by Elsevier Inc.)

In low- and middle-income countries, especially in Bangladesh, Autism Spectrum Disorder (ASD) may be considered an anathema, and social-cultural-financial constraints mean that there are few facilities available for treatment for ASD children. The revolution in the use of the mobile phone (~80%) by the majority of people in Bangladesh in recent years has created an opportunity to improve the overall scenario in the treatment or remote monitoring process for children with ASD. In this grant project, we planned and developed a mobile phone-based system to remotely monitor children with ASD and help their treatment process both at the caregiver and care practitioner ends. In developing mCARE, we utilized a Remote Experience Sampling Method to design, build, deploy, and study the impact of mobile based monitoring and treatment of children with ASD in Bangladesh. We developed a mobile application using the Experience Sampling Method (ESM). A caregiver routinely reported the behavioral and milestone parameters of their children with ASD. The care practitioners monitored the longitudinal data that helped them in decision-making in a particular patient's treatment process. The Value Sensitive Design (VSD) was used to make this mobile application more user friendly with consideration of the local economic, social, and cultural values in Bangladesh., Competing Interests: CONFLICT OF INTEREST The authors declare that they have no conflicts of interest associated with this project.

Background: Considering morbidity, mortality, and annual treatment costs, the dramatic rise in the incidence of sepsis and septic shock among intensive care unit (ICU) admissions in US hospitals is an increasing concern. Recent changes in the sepsis definition (sepsis-3), based on the quick Sequential Organ Failure Assessment (qSOFA), have motivated the international medical informatics research community to investigate score recalculation and information retrieval, and to study the intersection between sepsis-3 and the previous definition (sepsis-2) based on systemic inflammatory response syndrome (SIRS) parameters., Objective: The objective of this study was three-fold. First, we aimed to unpack the most prevalent criterion for sepsis (for both sepsis-3 and sepsis-2 predictors). Second, we intended to determine the most prevalent sepsis scenario in the ICU among 4 possible scenarios for qSOFA and 11 possible scenarios for SIRS. Third, we investigated the multicollinearity or dichotomy among qSOFA and SIRS predictors., Methods: This observational study was conducted according to the most recent update of Medical Information Mart for Intensive Care (MIMIC-III, Version 1.4), the critical care database developed by MIT. The qSOFA (sepsis-3) and SIRS (sepsis-2) parameters were analyzed for patients admitted to critical care units from 2001 to 2012 in Beth Israel Deaconess Medical Center (Boston, MA, USA) to determine the prevalence and underlying relation between these parameters among patients undergoing sepsis screening. We adopted a multiblind Delphi method to seek a rationale for decisions in several stages of the research design regarding handling missing data and outlier values, statistical imputations and biases, and generalizability of the study., Results: Altered mental status in the Glasgow Coma Scale (59.28%, 38,854/65,545 observations) was the most prevalent sepsis-3 (qSOFA) criterion and the white blood cell count (53.12%, 17,163/32,311 observations) was the most prevalent sepsis-2 (SIRS) criterion confronted in the ICU. In addition, the two-factored sepsis criterion of high respiratory rate (≥22 breaths/minute) and altered mental status (28.19%, among four possible qSOFA scenarios besides no sepsis) was the most prevalent sepsis-3 (qSOFA) scenario, and the three-factored sepsis criterion of tachypnea, high heart rate, and high white blood cell count (12.32%, among 11 possible scenarios besides no sepsis) was the most prevalent sepsis-2 (SIRS) scenario in the ICU. Moreover, the absolute Pearson correlation coefficients were not significant, thereby nullifying the likelihood of any linear correlation among the critical parameters and assuring the lack of multicollinearity between the parameters. Although this further bolsters evidence for their dichotomy, the absence of multicollinearity cannot guarantee that two random variables are statistically independent., Conclusions: Quantifying the prevalence of the qSOFA criteria of sepsis-3 in comparison with the SIRS criteria of sepsis-2, and understanding the underlying dichotomy among these parameters provides significant inferences for sepsis treatment initiatives in the ICU and informing hospital resource allocation. These data-driven results further offer design implications for multiparameter intelligent sepsis prediction in the ICU., (©Nazmus Sakib, Sheikh Iqbal Ahamed, Rumi Ahmed Khan, Paul M Griffin, Md Munirul Haque. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 03.12.2020.)

Aim: The poor quality and limited extent of palliative care services are of concern across the globe. To identify and measure patients' symptoms in Nepal, using a cell phone questionnaire platform, the investigators conducted and previously reported a cross-sectional study of Nepali adults. The unreported details of pain and other symptoms in these study data are here considered together with possible explanations and implications for interventions to lessen these symptoms., Methods: In a "snapshot" cross-sectional study of patients under regular care in three tertiary care Nepalese centers, we questioned 383 patients with incurable cancers using a 15-item cell phone-validated instrument to describe their major current symptoms and their intensities. The distributions of 11 symptom-level scores and the correlations between pain and different symptom scores were determined., Results: Thirty-eight percent of the population (142/383) had maximal pain scores which were in the severe range, and 25% (97/383) had such scores where they were evaluated. Patients reported moderate-to-severe tiredness 48% (183/383), depression 45% (172/383), anxiety 56% (217/383), poor appetite 64% (246/383), sleep quantity 64% (246/383), and sleep quality 64% (247/383)., Conclusions: The significant fractions of patients with severe maximal and at-evaluation pain scores suggest that inadequate recognition and treatment of such symptoms characterized care of these regularly seen patients. The high fractions of patients with mood and sleep disturbances support this reading, suggesting helplessness and hopelessness, all addressable with psychosocial, environmental, and nontoxic, inexpensive pharmacological interventions., Competing Interests: There are no conflicts of interest., (Copyright: © 2020 Indian Journal of Palliative Care.)

Purpose: This paper describes the evaluation of the implementation of an innovative teaching method, the "Engaging Parents in Education for Discharge" (ePED) iPad application (app), at a pediatric hospital., Design and Methods: The Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework was used to guide the evaluation. Three of the five RE-AIM elements are addressed in this study: Reach, Adoption, and Implementation., Results: The Reach of the ePED was 245 of 1015 (24.2%) patient discharges. The Adoption rate was 211 of 245 (86%) patients discharged in the five months' study period. High levels of fidelity (89.3%) to Implementation of the ePED were attained: the Signs and Symptoms domain had the highest (93%) and Thinking Forward about Family Adjustment screen had the lowest fidelity (83.3%). Nurse themes explained implementation fidelity: "It takes longer", and "Forgot to do it.", Conclusions: The ePED app operationalized how to have an engaging structured discharge conversation with parents. While the Reach of the ePED app was low under the study conditions, the adoption rate was positive. Nurses were able to integrate a theory-driven practice change into their daily routine when using the ePED app., Implications for Practice: The rates of adoption and implementation fidelity support the feasibility of future hospital wide implementation to improve patient and family healthcare experience. Attention to training of new content and the interactive conversation approach will be needed to fully leverage the value of the ePED app. Future studies are needed to evaluate the maintenance of the ePED app., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: As technology has advanced over the last decade, handheld Mobile Health (mHealth) applications have increased in popularity. Pregnancy is one area of mHealth that has rapidly expanded, however very few pregnancy apps are developed in collaboration with health professionals. This creates an environment where the pregnancy information women are accessing may be inaccurate or even dangerous. Additionally, there are relatively few medical apps devoted to prematurity or targeted to women at risk for premature birth. To address the gap in premature birth education, we assembled a multidisciplinary team, including health care professionals, and developed the Preemie Prep for Parents (P3) app., Methods: Our team previously conducted 5 focus group meetings to assess the information needs of our target audience. Based on this information we developed a low fidelity P3 prototype. Our software development team transferred the low fidelity prototype into a high fidelity prototype which was hosted on Test Flight (a beta testing platform). We performed heuristic evaluation as well as user testing to improve the P3 app., Results: User testing of the high fidelity P3 prototype was performed with 13 diverse participants. 6 participants were parents of currently admitted Neonatal Intensive Care Unit (NICU) babies and 7 participants were women who had been or were currently pregnant. The native language of participants included English, Spanish, and Hmong and their educational level varied between completing high school and graduate degree. Participants provided feedback on the content of the P3 app, as well as its organization and aesthetics. The feedback led to 83 iterations of the P3 app prior to its deployment. Overall, participants noted that the information was "informative" and "reliable". They also noted that the P3 app provided control over the information they could view and when they viewed it, stating "I could see info on my time". Overall, participants felt that the P3 app was a valuable tool for mothers in preterm labor and it would help them ask questions., Conclusions: Development of a mHealth app provides unique challenges regarding content, reliability of information, organization, and aesthetics. Creation of the P3 app to address the educational needs of women at risk for premature birth required assembling a multidisciplinary team, which included target users, and implementing an iterative design process. The efficacy of this app in improving user knowledge and decreasing anxiety is currently being tested in a randomized controlled trial., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Purpose: The purpose of this study was to evaluate the use of the Engaging Parents in Education for Discharge (ePED) iPad application on parent experiences of hospital discharge teaching and care coordination. Hypotheses were: parents exposed to discharge teaching using ePED will have 1) higher quality of discharge teaching and 2) better care coordination than parents exposed to usual discharge teaching. The secondary purpose examined group differences in the discharge teaching, care coordination, and 30-day readmissions for parents of children with and without a chronic condition., Design/methods: Using a quasi-experimental design, ePED was implemented on one inpatient unit (n = 211) and comparison group (n = 184) from a separate unit at a pediatric academic medical center. Patient experience outcome measures collected on day of discharge included Quality of Discharge Teaching Scale-Delivery (QDTS-D) and care coordination measured by Care Transition Measure (CTM). Thirty-day readmission was abstracted from records., Results: Parents taught using ePED reported higher QDTS-D scores than parents without ePED (p = .002). No differences in CTM were found between groups. Correlations between QDTS-D and CTM were small for ePED (r = 0.14, p 0.03) and non-ePED (r = 0.29, p < .001) parent groups. CTM was weakly associated with 30-day readmissions in the ePED group., Conclusion: The use of ePED by the discharging nurse enhances parent-reported quality of discharge teaching., Practice Implications: The ePED app is a theory-based structured conversation guide to engage parents in discharge preparation. Nursing implementation of ePED contributes to optimizing the patient/family healthcare experience., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Solid-organ transplantation is the treatment of choice for end-stage organ failure. Parents of pediatric transplant recipients who reported a lack of readiness for discharge had more difficulty coping and managing their child's medically complex care at home. In this paper, we describe the protocol for the pilot study of a mHealth intervention (myFAMI). The myFAMI intervention is based on the Individual and Family Self-Management Theory and focuses on family self-management of pediatric transplant recipients at home. The purpose of the pilot study is to test the feasibility of the myFAMI intervention with family members of pediatric transplant recipients and to test the preliminary efficacy on postdischarge coping through a randomized controlled trial. The sample will include 40 family units, 20 in each arm of the study, from three pediatric transplant centers in the United States. Results from this study may advance nursing science by providing insight for the use of mHealth to facilitate patient/family-nurse communication and family self-management behaviors for family members of pediatric transplant recipients., (© 2020 Wiley Periodicals, Inc.)

Background: Parents at risk for preterm birth frequently receive prematurity education when the mother is hospitalized for premature labor. Parental ability to learn and consider the information is limited because of the stress of the hospitalization. A promising approach is dissemination of information to at-risk parents before the birth hospitalization., Purpose: This article describes formative research used to develop smartphone-based prematurity education app for parents at-risk for preterm birth., Methods: Stakeholders were parents with a prior preterm birth. Using stakeholder meeting transcripts, constant comparative analysis was used to reflect upon the parental voice., Results: The parents named the app, Preemie Prep for Parents (P3). Parent perspectives revealed desire for information in the following 5 categories. (1) Power in knowledge and control: parents want autonomy when learning information that may influence medical decision-making. (2) Content and framing of information: they desire information from a trusted resource that helps promote prenatal health and provides neonatal intensive care information. (3) Displaying content: parents want personalization, push notifications, photographs displaying fetal development, and easy-to-understand statistics. (4) Providing information without causing harm: they desire non-value-laden information, and they do not support "gamifying" the app to enhance utilization. (5) Decision making: parents want information that would benefit their decision making without assuming that parents have a certain outlook on life or particular values., Implications for Practice: These findings support the need for the P3 App to aid in decision making when parents experience preterm birth., Implications for Research: The findings highlight the need to study the effects of smartphone-based prematurity education on medical decision-making.

Background: Lung cancer is an important public health issue, particularly among American Indians (AIs). The reported decline in tobacco use for most racial/ethnic groups is not observed among AIs. This project was designed to address the research question, "Why don't more Northern Plains American Indians alter tobacco use behaviors known to increase the risk of cancer?", Methods: Guided by the Theory of Planned Behavior, a multi-component intervention study was implemented. Adult AIs, age 18 years or older and currently smoking, were enrolled. Eligible subjects were randomized to one of 15 groups and exposed to either a MINIMAL or an INTENSE level of 4 intervention components. The intervention was delivered face-to-face or via telephone by Patient Navigators (PN). The primary outcome was self-reported abstinence from smoking verified by carbon monoxide measurement., Results: At 18 months post-quit date, 88% of those who were still in the study were abstinent. This included 6% of all participants who enrolled in the study (14/254) and 13% of those who made it to the quit date (14/108). No intervention groups were found to have significant proportions of participants who were abstinent from smoking at the quit date (visit 5) or primary outcome visit (18 months post-quit date, visit 11), but use of pharmacologic support for abstinence was found to be an effective strategy for individuals who continued participation throughout the study. Those who remained in the study received more visits and were more likely to be abstinent., Conclusions: Use of NRT increased the odds of not smoking, as assessed at the 18-month follow-up visit, but no other interventions were found to significantly contribute to abstinence from smoking. Although the intervention protocol included numerous points of contact between CRRs and participants (11 visits) loss to follow-up was extensive with only 16/254 remaining enrolled. Additional research is needed to improve understanding of factors that influence enrollment and retention in smoking cessation interventions for AI and other populations., (Copyright © 2019. Published by Elsevier Inc.)