Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies.

OBJECTIVE: Suicide is a complex and multifactorial public health problem. Understanding and addressing the various factors associated with suicide is crucial for prevention and intervention efforts. Machine learning (ML) could enhance the prediction of suicide attempts. METHOD: A systematic review was performed using PubMed, Scopus, Web of Science and SID databases. We aim to evaluate the performance of ML algorithms and summarize their effects, gather relevant and reliable information to synthesize existing evidence, identify knowledge gaps, and provide a comprehensive list of the suicide risk factors using mixed method approach. RESULTS: Forty-one studies published between 2011 and 2022, which matched inclusion criteria, were chosen as suitable. We included studies aimed at predicting the suicide risk by machine learning algorithms except natural language processing (NLP) and image processing. The neural network (NN) algorithm exhibited the lowest accuracy at 0.70, whereas the random forest demonstrated the highest accuracy, reaching 0.94. The study assessed the COX and random forest models and observed a minimum area under the curve (AUC) value of 0.54. In contrast, the XGBoost classifier yielded the highest AUC value, reaching 0.97. These specific AUC values emphasize the algorithm-specific performance in capturing the trade-off between sensitivity and specificity for suicide risk prediction. Furthermore, our investigation identified several common suicide risk factors, including age, gender, substance abuse, depression, anxiety, alcohol consumption, marital status, income, education, and occupation. This comprehensive analysis contributes valuable insights into the multifaceted nature of suicide risk, providing a foundation for targeted preventive strategies and intervention efforts. CONCLUSIONS: The effectiveness of ML algorithms and their application in predicting suicide risk has been controversial. There is a need for more studies on these algorithms in clinical settings, and the related ethical concerns require further clarification.

The most used questionnaires for evaluating the usability of robots and smart wearables: A scoping review.

Background: As the field of robotics and smart wearables continues to advance rapidly, the evaluation of their usability becomes paramount. Researchers may encounter difficulty in finding a suitable questionnaire for evaluating the usability of robotics and smart wearables. Therefore, the aim of this study is to identify the most commonly utilized questionnaires for assessing the usability of robots and smart wearables. Methods: A comprehensive search of databases, including PubMed, Web of Science, and Scopus, was conducted for this scoping review. Two authors performed the selection of articles and data extraction using a 10-field data extraction form. In cases of disagreements, a third author was consulted to reach a consensus. The inclusions were English-language original research articles that utilized validated questionnaires to assess the usability of healthcare robots and smart wearables. The exclusions comprised review articles, non-English publications, studies not focused on usability, those assessing clinical outcomes, articles lacking questionnaire details, and those using non-validated or researcher-made questionnaires. Descriptive statistics methods (frequency and percentage), were employed to analyze the data. Results: A total of 314 articles were obtained, and after eliminating irrelevant and duplicate articles, a final selection of 50 articles was included in this review. A total of 17 questionnaires were identified to evaluate the usability of robots and smart wearables, with 10 questionnaires specifically for wearables and 7 questionnaires for robots. The System Usability Scale (50%) and Post-Study System Usability Questionnaire (19.44%) were the predominant questionnaires utilized to assess the usability of smart wearables. Moreover, the most commonly used questionnaires for evaluating the usability of robots were the System Usability Scale (56.66%), User Experience Questionnaire (16.66%), and Quebec User Evaluation of Satisfaction with Assistive Technology (10%). Conclusion: Commonly employed questionnaires serve as valuable tools in assessing the usability of robots and smart wearables, aiding in the refinement and optimization of these technologies for enhanced user experiences. By incorporating user feedback and insights, designers can strive towards creating more intuitive and effective robotic and wearable solutions.

Identifying mitigation strategies of comprehensive health centers against dust hazard: a qualitative study in Iran.

BACKGROUND: Exposure to dust can disrupt healthcare services and severely affect all activity domains of the health system. The aim of this study was to explore mitigation strategies for comprehensive health centers against dust hazard. METHOD: The present study was conducted using a qualitative design with a conventional content analysis approach in 2023. The participants in this study were managers and staff of comprehensive health centers and experts in health in disasters and emergencies in Kerman, Bam, Regan, and Ahvaz. Data were collected through interviews. Data collection continued until data saturation. The collected data were analyzed based on the steps proposed by Graneheim and Lundman. Participants' statements, after recording and transcribing, were categorized into semantic units. Data were analyzed by using MAXQDA software version 2020. RESULTS: The analysis of the data with 23 participants revealed 106 Codes, 13 sub- categories and 5 main categories including: (A) reducing the impact of dust hazards, (B) management functions, (C) empowerment and performance improvement, (D) maintaining and promoting safety, and (E) Inter-sectoral coordination to implement mitigation strategies. CONCLUSION: The findings showed that the mitigation strategies and solutions can be used by health policymakers and planners to reduce the impact of dust hazard, empower and motivate healthcare staff, develop training protocols to enhance risk perception of the staff and members of the community, create the necessary infrastructure for adoption of effective mitigation strategies in healthcare centers to create resilience and continue service delivery.

Clinical decision support systems (CDSS) in assistance to COVID-19 diagnosis: A scoping review on types and evaluation methods.

Background and Aims: Due to the COVID-19 pandemic, a precise and reliable diagnosis of this disease is critical. The use of clinical decision support systems (CDSS) can help facilitate the diagnosis of COVID-19. This scoping review aimed to investigate the role of CDSS in diagnosing COVID-19. Methods: We searched four databases (Web of Science, PubMed, Scopus, and Embase) using three groups of keywords related to CDSS, COVID-19, and diagnosis. To collect data from studies, we utilized a data extraction form that consisted of eight fields. Three researchers selected relevant articles and extracted data using a data collection form. To resolve any disagreements, we consulted with a fourth researcher. Results: A search of the databases retrieved 2199 articles, of which 68 were included in this review after removing duplicates and irrelevant articles. The studies used nonknowledge-based CDSS (n = 52) and knowledge-based CDSS (n = 16). Convolutional Neural Networks (CNN) (n = 33) and Support Vector Machine (SVM) (n = 8) were employed to design the CDSS in most of the studies. Accuracy (n = 43) and sensitivity (n = 35) were the most common metrics for evaluating CDSS. Conclusion: CDSS for COVID-19 diagnosis have been developed mainly through machine learning (ML) methods. The greater use of these techniques can be due to their availability of public data sets about chest imaging. Although these studies indicate high accuracy for CDSS based on ML, their novelty and data set biases raise questions about replacing these systems as clinician assistants in decision-making. Further studies are needed to improve and compare the robustness and reliability of nonknowledge-based and knowledge-based CDSS in COVID-19 diagnosis.

Efficacy of virtual reality-based training programs and games on the improvement of cognitive disorders in patients: a systematic review and meta-analysis.

INTRODUCTION: Cognitive impairments present challenges for patients, impacting memory, attention, and problem-solving abilities. Virtual reality (VR) offers innovative ways to enhance cognitive function and well-being. This study explores the effects of VR-based training programs and games on improving cognitive disorders. METHODS: PubMed, Scopus, and Web of Science were systematically searched until May 20, 2023. Two researchers selected and extracted data based on inclusion and exclusion criteria, resolving disagreements through consultation with two other authors. Inclusion criteria required studies of individuals with any cognitive disorder engaged in at least one VR-based training session, reporting cognitive impairment data via scales like the MMSE. Only English-published RCTs were considered, while exclusion criteria included materials not primarily focused on the intersection of VR and cognitive disorders. The risk of bias in the included studies was assessed using the MMAT tool. Publication bias was assessed using funnel plots and Egger's test. The collected data were utilized to calculate the standardized mean differences (Hedges's g) between the treatment and control groups. The heterogeneity variance was estimated using the Q test and I2 statistic. The analysis was conducted using Stata version 17.0. RESULTS: Ten studies were included in the analysis out of a total of 3,157 retrieved articles. VR had a statistically significant improvement in cognitive impairments among patients (Hedges's g = 0.42, 95% CI: 0.15, 0.68; p_value = 0.05). games (Hedges's g = 0.61, 95% CI: 0.30, 0.39; p_value = 0.20) had a more significant impact on cognitive impairment improvement compared to cognitive training programs (Hedges's g = 0.29, 95% CI: -0.11, 0.69; p_value = 0.24). The type of VR intervention was a significant moderator of the heterogeneity between studies. CONCLUSION: VR-based interventions have demonstrated promise in enhancing cognitive function and addressing cognitive impairment, highlighting their potential as valuable tools in improving care for individuals with cognitive disorders. The findings underscore the relevance of incorporating virtual reality into therapeutic approaches for cognitive disorders.

Development of the Dardashna Checklist for Identifying Triggers of Behavioral Change in Individuals With Dementia: A Qualitative Study.

The purpose of the current qualitative study was to describe the development of the Dardashna Checklist to clinically identify behavior change triggers in individuals with dementia. Semi-structured, in-depth, face-to-face interviews were conducted with four physicians and four experienced caregivers involved in the care of individuals with dementia. From analysis of participants' interviews, themes extracted included Triggers of Behavioral Change in Individuals With Dementia and Types of Behavioral Changes, using the checklist structure as a guide. The information gathered by this checklist conveys important messages to experienced physicians or caregivers who want to help less experienced caregivers or individuals with dementia. In this case, physicians' prescriptions and the responses of other experienced caregivers will be more targeted and useful. This checklist will help facilitate clinical care decisions, improve quality of life, reduce expenses and side effects of medications, and improve communication among persons with dementia, their caregivers, and health care providers. [Journal of Gerontological Nursing, 50(1), 37-46.].

The effect of the holy Quran recitation and listening on anxiety, stress, and depression: A scoping review on outcomes.

Background and Aim: Anxiety, stress, and depressive disorders as common mental health problems have adverse effects in different populations. Holy Quran recitation and listening can help reduce these disorders. Therefore, the aim of this study was to investigate the effect of the Holy Quran on anxiety, stress, and depression. Materials and Methods: To retrieve eligible studies, we searched PubMed, Web of Science, and Scopus databases. The articles were screened and chosen by three researchers. The selection of studies and the data extraction from the studies were done by three researchers using the data collection form based on the inclusion and exclusion criteria. Disagreements were resolved by consulting the third and fourth researchers. To report scoping review, we used the PRISMA cheklist. Results: A total of 174 articles were retrieved from three databases and after removing irrelevant and repetitive articles, 15 articles were included in the current review. All studies were performed in Asia countries. Most studies have examined the effect of Holy Quran recitation and listening on anxiety (45%), stress (30%), and then depression (25%), respectively. The Beck Depression Inventory was the most widely used tool to evaluate the effect of Holy Quran recitation and listening on reducing anxiety, depression and stress. "Reducing the level of anxiety, stress, and depression" and "Simple, affordable, practical and cost-effective treatment to reduce depression and anxiety" were the most important outcomes of holy Quran recitation. Conclusions: Based on the results of this study, Quran recitation and listening can be applied as a useful nonpharmacological treatment to reduce anxiety, stress, and depression.

Medication adherence in patients with mental disorders: A systematic review and meta-analysis of telemedicine interventions.

BACKGROUND AND OBJECTIVE: Telemedicine interventions have emerged as a promising solution to improve medication adherence by providing remote support and monitoring of patients with mental disorders. This study aims to investigate the effectiveness of telemedicine interventions in enhancing medication adherence among patients with mental disorders. METHODS: PubMed, Scopus, and Web of Science were searched systematically. After deleting the double-included studies, two researchers independently selected articles and extracted data using a standardized data collection form. The risk of bias in the included studies was assessed using the Mixed Methods Appraisal Tool. The intervention effects were combined using a random effects model. Standardized mean differences (Hedges's g) between the treatment and control groups were calculated. Heterogeneity variance was estimated using the Q test and I2 statistic. The analysis was performed in Stata version 17.0. RESULTS: Out of the 1088 articles retrieved, nine studies were included in the analysis. Overall, telemedicine interventions demonstrated a statistically significant improvement in medication adherence among patients with mental disorders (Hedges' g = 0.25, 95% confidence interval: 0.12-0.38, p-value: < 0.01). The type of mental disorder was a significant moderator of the heterogeneity between studies (p = 0.022). CONCLUSION: Telemedicine interventions have a positive impact on medication adherence in patients with mental disorders by offering remote support and monitoring. Integrating telemedicine into mental healthcare can enhance overall adherence rates, leading to improved management of mental disorders.

Patients' perspectives and preferences toward telemedicine versus in-person visits: a mixed-methods study on 1226 patients.

INTRODUCTION: Despite the fact that telemedicine can eliminate geographical and time limitations and offer the possibility of diagnosing, treating, and preventing diseases by sharing reliable information, many individuals still prefer to visit medical centers for in-person consultations. The aim of this study was to determine the level of acceptance of telemedicine compared to in-person visits, identify the perceived advantages of telemedicine over in-person visits, and to explore the reasons why patients choose either of these two types of visits. METHODS: We developed a questionnaire using the rational method. The questionnaire consisted of multiple-choice questions and one open-ended question. A total of 2059 patients were invited to participate in the study. Chi-square tests and descriptive statistics were employed for data analysis. To analyze the data from the open-ended question, we conducted qualitative content analysis using MAXQDA 18. RESULTS: Out of the 1226 participants who completed the questionnaire, 865 (71%) preferred in-person visits, while 361 (29%) preferred telemedicine. Factors such as education level, specific health conditions, and prior experience with telemedicine influenced the preference for telemedicine. The participants provided a total of 183 different reasons for choosing either telemedicine (108 reasons) or in-person visits (75 reasons). Avoiding infectious diseases, saving cost, and eliminating and overcoming geographical distance barriers were three primary telemedicine benefits. The primary reasons for selecting an in-person visit were: more accurate diagnosis of the disease, more accurate and better examination of the patient by the physician, and more accurate and better treatment of the disease. CONCLUSION: The results demonstrate that despite the numerous benefits offered by telemedicine, the majority of patients still exhibit a preference for in-person visits. In order to promote broader acceptance of telemedicine, it becomes crucial for telemedicine services to address patient preferences and concerns effectively. Employing effective change management strategies can aid in overcoming resistance and facilitating the widespread adoption of telemedicine within the population.

Computer-aided diagnosis of keratoconus through VAE-augmented images using deep learning.

Detecting clinical keratoconus (KCN) poses a challenging and time-consuming task. During the diagnostic process, ophthalmologists are required to review demographic and clinical ophthalmic examinations in order to make an accurate diagnosis. This study aims to develop and evaluate the accuracy of deep convolutional neural network (CNN) models for the detection of keratoconus (KCN) using corneal topographic maps. We retrospectively collected 1758 corneal images (978 normal and 780 keratoconus) from 1010 subjects of the KCN group with clinically evident keratoconus and the normal group with regular astigmatism. To expand the dataset, we developed a model using Variational Auto Encoder (VAE) to generate and augment images, resulting in a dataset of 4000 samples. Four deep learning models were used to extract and identify deep corneal features of original and synthesized images. We demonstrated that the utilization of synthesized images during training process increased classification performance. The overall average accuracy of the deep learning models ranged from 99% for VGG16 to 95% for EfficientNet-B0. All CNN models exhibited sensitivity and specificity above 0.94, with the VGG16 model achieving an AUC of 0.99. The customized CNN model achieved satisfactory results with an accuracy and AUC of 0.97 at a much faster processing speed compared to other models. In conclusion, the DL models showed high accuracy in screening for keratoconus based on corneal topography images. This is a development toward the potential clinical implementation of a more enhanced computer-aided diagnosis (CAD) system for KCN detection, which would aid ophthalmologists in validating the clinical decision and carrying out prompt and precise KCN treatment.

Facilitators and barriers of mHealth interventions during the Covid-19 pandemic: systematic review.

BACKGROUND: With the spread of Covid-19 disease, health interventions related to the control, prevention, and treatment of this disease and other diseases were given real attention. The purpose of this systematic review is to express facilitators and barriers of using mobile health (mHealth) interventions during the Covid-19 pandemic. METHODS: In this systematic review, original studies were searched using keywords in the electronic database of PubMed until August 2022. The objectives and outcomes of these studies were extracted. Finally, to identify the facilitators and barriers of mHealth interventions, a qualitative content analysis was conducted based on the strengths, weaknesses, opportunities, and threats (SWOT) analysis method with Atlas.ti 8 software. We evaluated the studies using the Mixed Methods Appraisal Tool (MMAT). RESULTS: In total, 1598 articles were identified and 55 articles were included in this study. Most of the studies used mobile applications to provide and receive health services during the Covid-19 pandemic (96.4%). The purpose of the applications was to help prevention (17), follow-up (15), treatment (12), and diagnosis (8). Using SWOT analysis, 13 facilitators and 18 barriers to patients' use of mHealth services were identified. CONCLUSION: Mobile applications are very flexible technologies that can be customized for each person, patient, and population. During the Covid-19 pandemic, the applications designed due to lack of interaction, lack of time, lack of attention to privacy, and non-academic nature have not met their expectations of them.

Design and development of a mobile-based self-care application for patients with depression and anxiety disorders.

BACKGROUND AND AIM: Depression and anxiety can cause social, behavioral, occupational, and functional impairments if not controlled and managed. Mobile-based self-care applications can play an essential and effective role in controlling and reducing the effects of anxiety disorders and depression. The aim of this study was to design and develop a mobile-based self-care application for patients with depression and anxiety disorders with the goal of enhancing their mental health and overall well-being. MATERIALS AND METHODS: In this study we designed a mobile-based application for self -management of depression and anxiety disorders. In order to design this application, first the education- informational needs and capabilities were identified through a systematic review. Then, according to 20 patients with depression and anxiety, this education-informational needs and application capabilities were approved. In the next step, the application was designed. RESULTS: In the first step, 80 education-information needs and capabilities were identified. Finally, in the second step, of 80 education- informational needs and capabilities, 68 needs and capabilities with a mean greater than and equal to 3.75 (75%) were considered in application design. Disease control and management, drug management, nutrition and diet management, recording clinical records, communicating with physicians and other patients, reminding appointments, how to improve lifestyle, quitting smoking and reducing alcohol consumption, educational content, sedation instructions, introducing health care centers for depression and anxiety treatment and recording activities, personal goals and habits in a diary were the most important features of this application. CONCLUSION: The designed application can encourage patients with depression and stress to perform self-care processes and access necessary information without searching the Internet.

Machine learning for prediction of viral hepatitis: A systematic review and meta-analysis.

BACKGROUND: Lack of accurate and timely diagnosis of hepatitis poses obstacles to effective treatment, disease progression prevention, complication reduction, and life-saving interventions of patients. Utilizing machine learning can greatly enhance the achievement of timely and precise disease diagnosis. Therefore, we carried out this systematic review and meta-analysis to explore the performance of machine learning algorithms in predicting viral hepatitis. METHODS: Using an extensive literature search in PubMed, Scopus, and Web of Science databases until June 15, 2023, English publications on hepatitis prediction using machine learning algorithms were included. Two authors independently extracted pertinent information from the selected studies. The PRISMA 2020 checklist was followed for study selection and result reporting. The risk of bias was checked using the International Journal of Medical Informatics (IJMEDI) checklist. Data were analyzed using the 'metandi' command in Stata 17. RESULTS: Twenty-one original studies were included, covering 82 algorithms. Sixteen studies utilized five algorithms to predict hepatitis B. Ten studies used five algorithms for hepatitis C prediction. For hepatitis B prediction, the SVM algorithms demonstrated the highest sensitivity (90.0%; 95% confidence interval (CI): 77.0%-96.0%), specificity (94%; 95% CI: 90.0%-97.0%), and a diagnostic odds ratio (DOR) of 145 (95% CI: 37.0-559.0). In the case of hepatitis C, the KNN algorithms exhibited the highest sensitivity (80%; 95% CI:30.0%-97.0%), specificity (95%; 95% CI: 58.0%-99.0%), and DOR (72; 95% CI: 3.0-1644.0) for prediction. CONCLUSION: SVM and KNN demonstrated superior performance in predicting hepatitis. The proper algorithm along with clinical practice could improve hepatitis prediction and management.

Presenting a Quaternary Prevention Model for the Rural Family Physician Program in Iran with an Interpretive Structural Modeling Approach.

Background: Due to its ethical approach and its protection of patients and their interests, quaternary prevention can increase the quality-of-service provision and decrease costs and the wastage of resources. The present study used interpretive structural modeling (ISM) to classify the effective factors and determine a quaternary prevention model for Iran's Rural Family Physician Program. Methods: This study was a qualitative study with an ISM approach. Twenty-five health system experts and faculty members participated in the study. The interrelationships between the factors were determined using ISM, and after classification, the driving and dependence power of the factors were specified using MICMAC analysis. Results: The 20 factors were classified into five levels. The results indicated that patient interest and vulnerable groups had the highest effectiveness, and officials' and policymakers' commitment to providing serious support for family physicians had the highest affectability. The factors were placed into the two groups of linkage and dependence based on the MICMAC analysis. Conclusions: The new technologies are costly and sometimes only suitable for a specific group of patients. Costs and the issues of induced demand and defensive medicine necessitate a different view of health service distribution. The preventive and strategic view and the comprehensiveness of family physician services make quaternary prevention possible by providing high-risk and vulnerable groups with essential services based on patient needs and conditions with more benefit than harm.

Barriers and facilitators of using health information technologies by women: a scoping review.

BACKGROUND AND AIM: Health information technologies play a vital role in addressing diverse health needs among women, offering a wide array of services tailored to their specific requirements. Despite the potential benefits, the widespread utilization of these technologies by women faces numerous barriers and challenges. These barriers can cause women to either reduce their usage of health technologies or refrain from using them altogether. Therefore, this review was done with the aim of identifying and classifying barriers and facilitators. METHODS: Some databases, including PubMed, Web of Sciences, and Scopus were searched using related keywords. Then, according to the inclusion and exclusion criteria, the articles were evaluated and selected. Finally, the barriers and facilitators were identified and classified. RESULTS: Out of 14,399 articles, finally 35 articles were included in the review. In general, 375 barriers (232 items) and facilitators (143 items) were extracted from the studies. After merging similar items, 121 barriers (51 items) and facilitators (70 items) identified were organized into five main themes (management, technological, legal and regulatory, personal, and data and information management). The most important barriers were "privacy, confidentiality, and security concerns" (n = 24), "deficiencies and limitations of infrastructure, software, hardware, and network" (n = 19), "sociocultural challenges" (n = 15), and "poor economic status" (n = 15). Moreover, the most important facilitators were "increasing awareness, skills and continuous education of women" (n = 17, in personal theme), "providing training services" (n = 14, in management theme), "simple, usable, and user-friendly design of technologies" (n = 14, in technological theme), and "providing financial or non-financial incentives (motivation) for women" (n = 14, in personal theme). CONCLUSION: This review showed that in order to use technologies, women face many barriers, either specific to women (such as gender inequality) or general (such as lack of technical skills). To overcome these barriers, policymakers, managers of organizations and medical centers, and designers of health systems can consider the facilitators identified in this review.

A qualitative study of factors influencing ePHR adoption by caregivers and care providers of Alzheimer's patients: An extension of the unified theory of acceptance and use of technology model.

Background and Aims: As the nowadays provision of many healthcare services relies on technology, a better understanding of the factors contributing to the acceptance and use of technology in health care is essential. For Alzheimer's patients, an electronic personal health record (ePHR) is one such technology. Stakeholders should understand the factors affecting the adoption of this technology for its smooth implementation, adoption, and sustainable use. So far, these factors have not fully been understood for Alzheimer's disease (AD)-specific ePHR. Therefore, the present study aimed to understand these factors in ePHR adoption based on the perceptions and views of care providers and caregivers involved in AD care. Methods: This qualitative study was conducted from February 2020 to August 2021 in Kerman, Iran. Seven neurologists and 13 caregivers involved in AD care were interviewed using semi-structured and in-depth interviews. All interviews were conducted through phone contacts amid Covid-19 imposed restrictions, recorded, and transcribed verbatim. The transcripts were coded using thematic analysis based on the unified theory of acceptance and use of technology (UTAUT) model. ATLAS.ti8 was used for data analysis. Results: The factors affecting ePHR adoption in our study comprised subthemes under the five main themes of performance expectancy, effort expectancy, social influence, facilitating conditions of the UTAUT model, and the participants' sociodemographic factors. From the 37 facilitating factors and 13 barriers identified for ePHR adoption, in general, the participants had positive attitudes toward the ease of use of this system. The stated obstacles were dependent on the participants' sociodemographic factors (such as age and level of education) and social influence (including concern about confidentiality and privacy). In general, the participants considered ePHRs efficient and useful in increasing neurologists' information about their patients and managing their symptoms in order to provide better and timely treatment. Conclusion: The present study gives a comprehensive insight into the acceptance of ePHR for AD in a developing setting. The results of this study can be utilized for similar healthcare settings with regard to technical, legal, or cultural characteristics. To develop a useful and user-friendly system, ePHR developers should involve users in the design process to take into account the functions and features that match their skills, requirements, and preferences.

A data set for the design and implementation of the upper limb disability registry.

BACKGROUND: If the data elements needed for patient registries are not identified, designing and implementing them can be very challenging. Identifying and introducing a Data Set (DS) can help solve this challenge. The aim of this study was to identify and present a DS for the design and implementation of the upper limb disability registry. MATERIALS AND METHODS: This cross-sectional study was conducted in two phases. In the first phase, to identify the administrative and clinical data elements required for registry, a comprehensive study was conducted in PubMed, Web of Science, and Scopus databases. Then, the necessary data elements were extracted from the studies and a questionnaire was designed based on them. In the second phase, in order to confirm the DS, the questionnaire was distributed to 20 orthopedic, physical medicine and rehabilitation physicians and physiotherapists during a two-round Delphi. In order to analyze the data, the frequency and mean score of each data element were calculated. Data elements that received an agreement more than 75% in the first or two-round Delphi were considered for the final DS. RESULTS: A total of 81 data elements in five categories of "demographic data", "clinical presentation", "past medical history", "psychological issues", and "pharmacological and non-pharmacological treatments" were extracted from the studies. Finally, 78 data elements were approved by experts as essential data elements for designing a patient registry for upper limb disabilities. CONCLUSION: In this study, the data elements necessary for the design and implementation of the upper limb disability registry were suggested. This DS can help registry designers and health data administrators know what data needs to be included in the registry system in order to have a successful design and implementation. Moreover, this standardized DS can be effective for integrating and improving the information management of people with upper limb disabilities and used to accurately gather the upper limb disabilities data for research and policymaking purposes.

A Mobile Application to Assist Alzheimer's Caregivers During COVID-19 Pandemic: Development and Evaluation.

Introduction: Access to healthcare services for patients with Alzheimer's disease (AD) was limited during the COVID-19 pandemic. A mobile application (app) can help overcome this limitation for patients and caregivers. Our study aims to develop and evaluate an app to help caregivers of patients with AD during COVID-19. Methods: The study was performed in three steps. First, a questionnaire of features required for the app design was prepared based on the interviews with caregivers of AD patients and neurologists. Then, questionnaire was provided to neurologists, medical informatics, and health information management specialists to identify the final features. Second, the app was designed using the information obtained from the previous phase. Third, the quality of the app and the level of user satisfaction were evaluated using the mobile app rating scale (MARS) and the questionnaire for user interface satisfaction (QUIS), respectively. Results: The number of 41 data elements in four groups (patient's profile, COVID-19 management and control, AD management and control, and program functions) were identified for designing the app. The quality evaluation of the app based on MARS and user satisfaction evaluation based on QUIS showed the app was good. Conclusion: This is the first study that focused on developing and evaluating a mobile app for assisting Alzheimer's caregivers during the COVID-19 pandemic. As the app was designed based on users' needs and covered both information about AD and COVID-19, it can help caregivers perform their tasks more efficiently.

Digital games for rehabilitation of speech disorders: A scoping review.

Background and Aims: Digital games are among the treatment methods for speech disorders that serve purposes other than mere entertainment. These games have been used for different speech disorders at any age. This study aims to review articles that have used digital games for rehabilitating speech disorders. Methods: This study was a scoping review. PubMed, Scopus, and Web of Science were searched on February 28, 2022, to access the articles on digital games used in rehabilitation of speech disorders without any date restrictions. The search strategy was as follows: ("video game [MeSH term]" OR "computer game" OR "mobile game" OR "serious game" OR gamification [MeSH term]) AND ("speech pathology" OR "speech therapy [MeSH term]" OR "speech disorder [MeSH term]" OR stuttering [MeSH term]). Original interventional and observational studies in English were included. The data were extracted from the relevant articles, including the first author's name, year of publication, country, target group, participants, mobile device/computer-based, type of game design, language level, number of sessions, and outcome. Descriptive statistics were used to analyze the data. Results: Of 693 retrieved articles, 10 articles were included in this study. Digital games were used for different speech disorders such as apraxia (20%), dysarthria (10%), articulatory hypokinesia in Parkinson's disease (10%), dysphonic disorder (10%), hearing disability (10%), phonological impairment (10%), and speech disorder in autism (10%). Most of the articles (60%) used a mobile device-based game. Phonemes (30%), words (30%), and sentences (20%) were the most frequently used language levels in designing digital games. All the reviewed articles reported the positive effect of digital games on speech and the patients' motivation in therapy. Conclusion: Digital games can improve patients' speech and motivation in therapy. Although studies showed the positive impact of digital games on speech disorders, personalized speech therapy should be considered in designing these games.