Interactive Journal of Medical Research最新文献

{"title":"Narrowing the Digital Divide: Framework for Creating Telehealth Equity Dashboards.","authors":"Michael J Luke, Sansanee Craig, Suzinne Pak-Gorstein, Marlíse Arellano, Jessica Zhang, S Margaret Wright, John Chuo, Philip V Scribano","doi":"10.2196/57435","DOIUrl":"https://doi.org/10.2196/57435","url":null,"abstract":"<p><p>Telehealth presents both the potential to improve access to care and to widen the digital divide contributing to health care disparities and obliging health care systems to standardize approaches to measure and display telehealth disparities. Based on a literature review and the operational experience of clinicians, informaticists, and researchers in the Supporting Pediatric Research on Outcomes and Utilization of Telehealth (SPROUT)-Clinical and Translational Science Awards (CTSA) Network, we outline a strategic framework for health systems to develop and optimally use a telehealth equity dashboard through a 3-phased approach of (1) defining data sources and key equity-related metrics of interest; (2) designing a dynamic and user-friendly dashboard; and (3) deploying the dashboard to maximize engagement among clinical staff, investigators, and administrators.</p>","PeriodicalId":51757,"journal":{"name":"Interactive Journal of Medical Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142134410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Telemedicine Research Trends in 2001-2022 and Research Cooperation Between China and Other Countries Before and After the COVID-19 Pandemic: Bibliometric Analysis.","authors":"Hanlin Feng, Karin Kurata, Jianfei Cao, Kageyama Itsuki, Makoto Niwa, Atsushi Aoyama, Kota Kodama","doi":"10.2196/40801","DOIUrl":"https://doi.org/10.2196/40801","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Advancements in technology have overcome geographical barriers, making telemedicine, which offers remote emergency services, healthcare, and medication guidance, increasingly popular. COVID-19 restrictions amplified its global importance by bridging distances.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to analyze Chinese and global literature data, present new global telemedicine research trends, and clarify the development potential, collaborations, and deficiencies in China's telemedicine research.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted bibliometrics and network analyses on relevant documents from the Web of Science database from 2001 to 2022. Data collection was completed on October 30, 2023. Considering COVID-19's impact, 2020 was used as a baseline, dividing the data into 2 periods: 2001-2019 and 2020-2022. The development potential was determined based on publication trends. An international coauthorship network analysis identified collaboration statuses and potential. Co-occurrence analysis was conducted for China and the world.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We identified 25,333 telemedicine-related research papers published between 2001 and 2022, with a substantial increase during the COVID-19 period (2020-2022), particularly in China (1.93-fold increase), moving its global publication rank from tenth to sixth. The United States, the United Kingdom, and Australia contributed 62.96% of the literature, far ahead of China's 3.90%. Globally, telemedicine research increased significantly post-2020. Between 2001 and 2019, the United States and Australia were central in coauthor networks; post-2020, the United States remained the largest node. Network hubs included the United States, the United Kingdom, Australia, and Canada. Keyword co-occurrence analysis revealed 5 global clusters from 2001 to 2019 (system technology, health care applications, mobile health, mental health, and electronic health) and 2020 to 2022 (COVID-19, children's mental health, artificial intelligence, digital health, and rehabilitation of middle-aged and older adults). In China, the research trends aligned with global patterns, with rapid growth post-2020. From 2001 to 2019, China cooperated closely with Indonesia, India, Japan, Taiwan, and South Korea. From 2020 to 2022, cooperation expanded to Japan, Singapore, Malaysia, and South Korea, as well as Saudi Arabia, Egypt, South Africa, Ghana, Lebanon, and other African and Middle Eastern countries. Chinese keyword co-occurrence analysis showed focus areas in system technology, health care applications, mobile health, big data analysis, and electronic health (2001-2019) and COVID-19, artificial intelligence, digital health, and mental health (2020-2022). Although psychology research increased, studies on children's mental health and middle-aged and older adults' rehabilitation were limited.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;We identified the latest trends in telemedicine res","PeriodicalId":51757,"journal":{"name":"Interactive Journal of Medical Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142114659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parental Patterns of Alcohol Consumption During the COVID-19 Pandemic: Scoping Review.","authors":"Christine Ou, Kathryn Corby, Kelsey Booth, Hui-Hui Ou","doi":"10.2196/48339","DOIUrl":"https://doi.org/10.2196/48339","url":null,"abstract":"<p><strong>Background: </strong>The declaration of the COVID-19 pandemic led to public health restrictions that impacted the lives of people across the globe. Parents were particularly burdened with balancing multiple responsibilities, such as working from home while caring for and educating their children. Alcohol use among parents is an area that warrants further exploration.</p><p><strong>Objective: </strong>This study aimed to investigate patterns of parental alcohol consumption during the COVID-19 pandemic, focusing on relative changes in the frequency and quantity of alcohol use compared to prepandemic use, nonparent adult samples, or both.</p><p><strong>Methods: </strong>A scoping review informed by the methodology of Arksey and O'Malley explored patterns of parental alcohol consumption during the COVID-19 pandemic. Searches were conducted in CINAHL, Ovid MEDLINE, PsycINFO, and Web of Science. Search terms were created using the Joanna Briggs Institute framework of Population, Concept, and Context, with the population being parents and the concept being alcohol consumption during the COVID-19 pandemic.</p><p><strong>Results: </strong>The database search yielded 3568 articles, which were screened for eligibility. Of the 3568 articles, 40 (1.12%) met the inclusion criteria and were included in the scoping review. Findings indicated the following: (1) having children at home was a factor associated with parental patterns of alcohol use; (2) mixed findings regarding gender-related patterns of alcohol consumption; and (3) linkages between parental patterns of alcohol use and mental health symptoms of stress, depression, and anxiety.</p><p><strong>Conclusions: </strong>This scoping review revealed heterogeneous patterns in parental alcohol use across sociocultural contexts during the COVID-19 pandemic. Given the known harms of alcohol use, it is worthwhile for clinicians to assess parental drinking patterns and initiate conversations regarding moderation in alcohol use.</p>","PeriodicalId":51757,"journal":{"name":"Interactive Journal of Medical Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142074535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health and Well-Being Among College Students in the United States During the COVID-19 Pandemic: Daily Diary Study.","authors":"Stephanie T Lanza, Courtney Whetzel, Sandesh Bhandari","doi":"10.2196/45689","DOIUrl":"10.2196/45689","url":null,"abstract":"<p><strong>Background: </strong>There is evidence that anxiety and stress increased among college students during the COVID-19 pandemic. However, less is known about daily experiences of affect, worry, substance use behaviors, experiences of pleasure, concern over food security, experiences of bias or discrimination, feelings of belongingness, and other indicators of well-being and how they vary across days in this population.</p><p><strong>Objective: </strong>This study surveyed a wide range of indicators of health and well-being in daily life over 21 days with a sample of college students in a large university system in the United States during the pandemic. The overall variance in each daily measure was partitioned to reflect the proportion due to (1) between-person differences versus (2) within-person, day-to-day variability. This is important because measures that vary primarily due to between-person differences may be more amenable to interventions that target particular students, whereas measures that vary more due to day-to-day variability may be more amenable to interventions that target day-level contextual factors.</p><p><strong>Methods: </strong>A sample of 2068 young adult college students (aged 18-24, mean 19.8, SD 1.3 years; 66.6% women) completed a baseline survey; 97.3% (n=2012) then completed up to 21 consecutive daily surveys that assessed a comprehensive set of daily markers of health, behavior, and well-being. The daily diary study produced a total of 33,722 person-days.</p><p><strong>Results: </strong>Among all person-days, a minority were substance use days (eg, 14.5% of days involved alcohol use, 5.6% vaping, and 5.5% cannabis). Experiences of pleasure were reported on most (73.5%) days. Between-person differences explained more than 50% of the variance in numerous indicators of health and well-being, including daily vaping, cannabis use, other illicit substance use, experiences of bias or discrimination, positive affect, negative affect, worry, food insecurity, and feelings of belonging at the university. In contrast, within-person differences explained more than 50% of the variance in daily alcohol use, cigarette use, stress, experiences of pleasure, where the student slept last night, and physical activity.</p><p><strong>Conclusions: </strong>College student health and well-being are multifaceted, with some aspects likely driven by person-level characteristics and experiences and other aspects by more dynamic, contextual risk factors that occur in daily life. These findings implicate services and interventions that should target individual students versus those that should target days on which students are at high risk for poor experiences or behaviors.</p>","PeriodicalId":51757,"journal":{"name":"Interactive Journal of Medical Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11380054/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142037712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Establishment and Evaluation of a Noninvasive Metabolism-Related Fatty Liver Screening and Dynamic Monitoring Model: Cross-Sectional Study.","authors":"Jiali Ni, Yong Huang, Qiangqiang Xiang, Qi Zheng, Xiang Xu, Zhiwen Qin, Guoping Sheng, Lanjuan Li","doi":"10.2196/56035","DOIUrl":"10.2196/56035","url":null,"abstract":"<p><strong>Background: </strong>Metabolically associated fatty liver disease (MAFLD) insidiously affects people's health, and many models have been proposed for the evaluation of liver fibrosis. However, there is still a lack of noninvasive and sensitive models to screen MAFLD in high-risk populations.</p><p><strong>Objective: </strong>The purpose of this study was to explore a new method for early screening of the public and establish a home-based tool for regular self-assessment and monitoring of MAFLD.</p><p><strong>Methods: </strong>In this cross-sectional study, there were 1758 eligible participants in the training set and 200 eligible participants in the testing set. Routine blood, blood biochemistry, and FibroScan tests were performed, and body composition was analyzed using a body composition instrument. Additionally, we recorded multiple factors including disease-related risk factors, the Forns index score, the hepatic steatosis index (HSI), the triglyceride glucose index, total body water (TBW), body fat mass (BFM), visceral fat area, waist-height ratio (WHtR), and basal metabolic rate. Binary logistic regression analysis was performed to explore the potential anthropometric indicators that have a predictive ability to screen for MAFLD. A new model, named the MAFLD Screening Index (MFSI), was established using binary logistic regression analysis, and BFM, WHtR, and TBW were included. A simple rating table, named the MAFLD Rating Table (MRT), was also established using these indicators.</p><p><strong>Results: </strong>The performance of the HSI (area under the curve [AUC]=0.873, specificity=76.8%, sensitivity=81.4%), WHtR (AUC=0.866, specificity=79.8%, sensitivity=80.8%), and BFM (AUC=0.842, specificity=76.9%, sensitivity=76.2%) in discriminating between the MAFLD group and non-fatty liver group was evaluated (P<.001). The AUC of the combined model including WHtR, HSI, and BFM values was 0.900 (specificity=81.8%, sensitivity=85.6%; P<.001). The MFSI was established based on better performance at screening MAFLD patients in the training set (AUC=0.896, specificity=83.8%, sensitivity=82.1%) and was confirmed in the testing set (AUC=0.917, specificity=89.8%, sensitivity=84.4%; P<.001).</p><p><strong>Conclusions: </strong>The novel MFSI model was built using WHtR, BFM, and TBW to screen for early MAFLD. These body parameters can be easily obtained using a body fat scale at home, and the mobile device software can record specific values and perform calculations. MFSI had better performance than other models for early MAFLD screening. The new model showed strong power and stability and shows promise in the area of MAFLD detection and self-assessment. The MRT was a practical tool to assess disease alterations in real time.</p>","PeriodicalId":51757,"journal":{"name":"Interactive Journal of Medical Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11377904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emerging Indications for Hyperbaric Oxygen Treatment: Registry Cohort Study.","authors":"Hideaki L Tanaka, Judy R Rees, Ziyin Zhang, Judy A Ptak, Pamela M Hannigan, Elaine M Silverman, Janet L Peacock, Jay C Buckey","doi":"10.2196/53821","DOIUrl":"10.2196/53821","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Hyperbaric oxygen (HBO&lt;sub&gt;2&lt;/sub&gt;) treatment is used across a range of medical specialties for a variety of applications, particularly where hypoxia and inflammation are important contributors. Because of its hypoxia-relieving and anti-inflammatory effects HBO&lt;sub&gt;2&lt;/sub&gt; may be useful for new indications not currently approved by the Undersea and Hyperbaric Medical Society. Identifying these new applications for HBO&lt;sub&gt;2&lt;/sub&gt; is difficult because individual centers may only treat a few cases and not track the outcomes consistently. The web-based International Multicenter Registry for Hyperbaric Oxygen Therapy captures prospective outcome data for patients treated with HBO&lt;sub&gt;2&lt;/sub&gt; therapy. These data can then be used to identify new potential applications for HBO&lt;sub&gt;2&lt;/sub&gt;, which has relevance for a range of medical specialties.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;Although hyperbaric medicine has established indications, new ones continue to emerge. One objective of this registry study was to identify cases where HBO&lt;sub&gt;2&lt;/sub&gt; has been used for conditions falling outside of current Undersea and Hyperbaric Medical Society-approved indications and present outcome data for them.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This descriptive study used data from a web-based, multicenter, international registry of patients treated with HBO&lt;sub&gt;2&lt;/sub&gt;. Participating centers agree to collect data on all patients treated using standard outcome measures, and individual centers send deidentified data to the central registry. HBO&lt;sub&gt;2&lt;/sub&gt; treatment programs in the United States, the United Kingdom, and Australia participate. Demographic, outcome, complication, and treatment data, including pre- and posttreatment quality of life questionnaires (EQ-5D-5L) were collected for individuals referred for HBO&lt;sub&gt;2&lt;/sub&gt; treatment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Out of 9726 patient entries, 378 (3.89%) individuals were treated for 45 emerging indications. Post-COVID-19 condition (PCC; also known as postacute sequelae of COVID-19; 149/378, 39.4%), ulcerative colitis (47/378, 12.4%), and Crohn disease (40/378, 10.6%) accounted for 62.4% (n=236) of the total cases. Calciphylaxis (20/378, 5.3%), frostbite (18/378, 4.8%), and peripheral vascular disease-related wounds (12/378, 3.2%) accounted for a further 13.2% (n=50). Patients with PCC reported significant improvement on the Neurobehavioral Symptom Inventory (NSI score: pretreatment=30.6; posttreatment=14.4; P&lt;.001). Patients with Crohn disease reported significantly improved quality of life (EQ-5D score: pretreatment=53.8; posttreatment=68.8), and 5 (13%) reported closing a fistula. Patients with ulcerative colitis and complete pre- and post-HBO&lt;sub&gt;2&lt;/sub&gt; data reported improved quality of life and lower scores on a bowel questionnaire examining frequency, blood, pain, and urgency. A subset of patients with calciphylaxis and arterial ulcers also reported improvement.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conc","PeriodicalId":51757,"journal":{"name":"Interactive Journal of Medical Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11372337/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141794068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes: Scoping Review.","authors":"Jessica Rahman, Aida Brankovic, Mark Tracy, Sankalp Khanna","doi":"10.2196/46946","DOIUrl":"10.2196/46946","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Computational signal preprocessing is a prerequisite for developing data-driven predictive models for clinical decision support. Thus, identifying the best practices that adhere to clinical principles is critical to ensure transparency and reproducibility to drive clinical adoption. It further fosters reproducible, ethical, and reliable conduct of studies. This procedure is also crucial for setting up a software quality management system to ensure regulatory compliance in developing software as a medical device aimed at early preclinical detection of clinical deterioration.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This scoping review focuses on the neonatal intensive care unit setting and summarizes the state-of-the-art computational methods used for preprocessing neonatal clinical physiological signals; these signals are used for the development of machine learning models to predict the risk of adverse outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Five databases (PubMed, Web of Science, Scopus, IEEE, and ACM Digital Library) were searched using a combination of keywords and MeSH (Medical Subject Headings) terms. A total of 3585 papers from 2013 to January 2023 were identified based on the defined search terms and inclusion criteria. After removing duplicates, 2994 (83.51%) papers were screened by title and abstract, and 81 (0.03%) were selected for full-text review. Of these, 52 (64%) were eligible for inclusion in the detailed analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of the 52 articles reviewed, 24 (46%) studies focused on diagnostic models, while the remainder (n=28, 54%) focused on prognostic models. The analysis conducted in these studies involved various physiological signals, with electrocardiograms being the most prevalent. Different programming languages were used, with MATLAB and Python being notable. The monitoring and capturing of physiological data used diverse systems, impacting data quality and introducing study heterogeneity. Outcomes of interest included sepsis, apnea, bradycardia, mortality, necrotizing enterocolitis, and hypoxic-ischemic encephalopathy, with some studies analyzing combinations of adverse outcomes. We found a partial or complete lack of transparency in reporting the setting and the methods used for signal preprocessing. This includes reporting methods to handle missing data, segment size for considered analysis, and details regarding the modification of the state-of-the-art methods for physiological signal processing to align with the clinical principles for neonates. Only 7 (13%) of the 52 reviewed studies reported all the recommended preprocessing steps, which could have impacts on the downstream analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The review found heterogeneity in the techniques used and inconsistent reporting of parameters and procedures used for preprocessing neonatal physiological signals, which is necessary to confirm adherence to clinical and software quality management sy","PeriodicalId":51757,"journal":{"name":"Interactive Journal of Medical Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11372324/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142009890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medication Management Strategies to Support Medication Adherence: Interview Study With Older Adults.","authors":"Lisa Gualtieri, Mathilda Rigby, Deelia Wang, Elaine Mann","doi":"10.2196/53513","DOIUrl":"10.2196/53513","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Home medication management has been insufficiently studied, including the factors that impact the development and effectiveness of adherence strategies under both routine and anomalous circumstances. Older adults are a particularly important population to study due to the greater likelihood of taking medication in combination with the desire to \"age in place.\"&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This interview study aims to understand how older adults develop medication management strategies, identify when and why such strategies succeed or fail, learn more about how older adults think about their medication, and explore interventions that increase medication adherence.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study used a qualitative, semistructured interview design to elicit older adults' experiences with home medication management. Overall, 22 participants aged ≥50 years taking 1 to 3 prescription medications were recruited and interviewed. Interview responses were recorded, and thematic, qualitative analysis was performed by reviewing recordings and identifying recurring patterns and themes. Responses were systematically coded, which not only facilitated the identification of these themes but also allowed us to quantify the prevalence of behaviors and perceptions, providing a robust understanding of medication management and medication adherence.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Participants reported developing home medication management strategies on their own, with none of the participants receiving guidance from health care providers and 59% (13/22) of the participants using trial and error. The strategies developed by study participants were all unique and generally encompassed prescription medication and vitamins or supplements, with no demarcation between what was prescribed or recommended by a physician and what they selected independently. Participants thought about their medications by their chemical name (10/22, 45%), by the appearance of the pill (8/22, 36%), by the medication's purpose (2/22, 9%), or by the medication's generic name (2/22, 9%). Pill cases (17/22, 77%) were more popular than prescription bottles (5/22, 23%) for storage of daily medication. Most participants (19/22, 86%) stored their pill cases or prescription bottles in visible locations in the home, and those using pill cases varied in their refill routines. Participants used ≥2 routines or objects as triggers to take their medication. Nonadherence was associated with a disruption to their routine. Finally, only 14% (3/22) of the participants used a time-based reminder or alarm, and none of the participants used a medication adherence device or app.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Participants in our study varied considerably in their home medication management strategies and developed unique routines to remember to take their medication as well as to refill their pill cases. To reduce trial and error in establishing a strategy, there ","PeriodicalId":51757,"journal":{"name":"Interactive Journal of Medical Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11350294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141972265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Debate and Dilemmas Regarding Generative AI in Mental Health Care: Scoping Review.","authors":"Xuechang Xian, Angela Chang, Yu-Tao Xiang, Matthew Tingchi Liu","doi":"10.2196/53672","DOIUrl":"10.2196/53672","url":null,"abstract":"<p><strong>Background: </strong>Mental disorders have ranked among the top 10 prevalent causes of burden on a global scale. Generative artificial intelligence (GAI) has emerged as a promising and innovative technological advancement that has significant potential in the field of mental health care. Nevertheless, there is a scarcity of research dedicated to examining and understanding the application landscape of GAI within this domain.</p><p><strong>Objective: </strong>This review aims to inform the current state of GAI knowledge and identify its key uses in the mental health domain by consolidating relevant literature.</p><p><strong>Methods: </strong>Records were searched within 8 reputable sources including Web of Science, PubMed, IEEE Xplore, medRxiv, bioRxiv, Google Scholar, CNKI and Wanfang databases between 2013 and 2023. Our focus was on original, empirical research with either English or Chinese publications that use GAI technologies to benefit mental health. For an exhaustive search, we also checked the studies cited by relevant literature. Two reviewers were responsible for the data selection process, and all the extracted data were synthesized and summarized for brief and in-depth analyses depending on the GAI approaches used (traditional retrieval and rule-based techniques vs advanced GAI techniques).</p><p><strong>Results: </strong>In this review of 144 articles, 44 (30.6%) met the inclusion criteria for detailed analysis. Six key uses of advanced GAI emerged: mental disorder detection, counseling support, therapeutic application, clinical training, clinical decision-making support, and goal-driven optimization. Advanced GAI systems have been mainly focused on therapeutic applications (n=19, 43%) and counseling support (n=13, 30%), with clinical training being the least common. Most studies (n=28, 64%) focused broadly on mental health, while specific conditions such as anxiety (n=1, 2%), bipolar disorder (n=2, 5%), eating disorders (n=1, 2%), posttraumatic stress disorder (n=2, 5%), and schizophrenia (n=1, 2%) received limited attention. Despite prevalent use, the efficacy of ChatGPT in the detection of mental disorders remains insufficient. In addition, 100 articles on traditional GAI approaches were found, indicating diverse areas where advanced GAI could enhance mental health care.</p><p><strong>Conclusions: </strong>This study provides a comprehensive overview of the use of GAI in mental health care, which serves as a valuable guide for future research, practical applications, and policy development in this domain. While GAI demonstrates promise in augmenting mental health care services, its inherent limitations emphasize its role as a supplementary tool rather than a replacement for trained mental health providers. A conscientious and ethical integration of GAI techniques is necessary, ensuring a balanced approach that maximizes benefits while mitigating potential challenges in mental health care practices.</p>","PeriodicalId":51757,"journal":{"name":"Interactive Journal of Medical Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11347908/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141972264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resilience Informatics: Role of Informatics in Enabling and Promoting Public Health Resilience to Pandemics, Climate Change, and Other Stressors.","authors":"M Sriram Iyengar, Maiya G Block Ngaybe, Myla Gonzalez, Mona Arora","doi":"10.2196/54687","DOIUrl":"10.2196/54687","url":null,"abstract":"<p><p>Climate change, local epidemics, future pandemics, and forced displacements pose significant public health threats worldwide. To cope successfully, people and communities are faced with the challenging task of developing resilience to these stressors. Our viewpoint is that the powerful capabilities of modern informatics technologies including artificial intelligence, biomedical and environmental sensors, augmented or virtual reality, data science, and other digital hardware or software, have great potential to promote, sustain, and support resilience in people and communities. However, there is no \"one size fits all\" solution for resilience. Solutions must match the specific effects of the stressor, cultural dimensions, social determinants of health, technology infrastructure, and many other factors.</p>","PeriodicalId":51757,"journal":{"name":"Interactive Journal of Medical Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}