Journal of Medical Internet Research最新文献

{"title":"Looking Back on Digital Medical Education Over the Last 25 Years and Looking to the Future: Narrative Review.","authors":"Oluwadamilola Ogundiya, Thahmina Jasmine Rahman, Ioan Valnarov-Boulter, Tim Michael Young","doi":"10.2196/60312","DOIUrl":"https://doi.org/10.2196/60312","url":null,"abstract":"<p><strong>Background: </strong>The last 25 years have seen enormous progression in digital technologies across the whole of the health service, including health education. The rapid evolution and use of web-based and digital techniques have been significantly transforming this field since the beginning of the new millennium. These advancements continue to progress swiftly, even more so after the COVID-19 pandemic.</p><p><strong>Objective: </strong>This narrative review aims to outline and discuss the developments that have taken place in digital medical education across the defined time frame. In addition, evidence for potential opportunities and challenges facing digital medical education in the near future was collated for analysis.</p><p><strong>Methods: </strong>Literature reviews were conducted using PubMed, Web of Science Core Collection, Scopus, Google Scholar, and Embase. The participants and learners in this study included medical students, physicians in training or continuing professional development, nurses, paramedics, and patients.</p><p><strong>Results: </strong>Evidence of the significant steps in the development of digital medical education in the past 25 years was presented and analyzed in terms of application, impact, and implications for the future. The results were grouped into the following themes for discussion: learning management systems; telemedicine (in digital medical education); mobile health; big data analytics; the metaverse, augmented reality, and virtual reality; the COVID-19 pandemic; artificial intelligence; and ethics and cybersecurity.</p><p><strong>Conclusions: </strong>Major changes and developments in digital medical education have occurred from around the start of the new millennium. Key steps in this journey include technical developments in teleconferencing and learning management systems, along with a marked increase in mobile device use for accessing learning over this time. While the pace of evolution in digital medical education accelerated during the COVID-19 pandemic, further rapid progress has continued since the resolution of the pandemic. Many of these changes are currently being widely used in health education and other fields, such as augmented reality, virtual reality, and artificial intelligence, providing significant future potential. The opportunities these technologies offer must be balanced against the associated challenges in areas such as cybersecurity, the integrity of web-based assessments, ethics, and issues of digital privacy to ensure that digital medical education continues to thrive in the future.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e60312"},"PeriodicalIF":5.8,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142864589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Machine Learning-Based Prediction Model for Acute Kidney Injury in Patients With Community-Acquired Pneumonia: Multicenter Validation Study.","authors":"Mengqing Ma, Caimei Chen, Dawei Chen, Hao Zhang, Xia Du, Qing Sun, Li Fan, Huiping Kong, Xueting Chen, Changchun Cao, Xin Wan","doi":"10.2196/51255","DOIUrl":"10.2196/51255","url":null,"abstract":"<p><strong>Background: </strong>Acute kidney injury (AKI) is common in patients with community-acquired pneumonia (CAP) and is associated with increased morbidity and mortality.</p><p><strong>Objective: </strong>This study aimed to establish and validate predictive models for AKI in hospitalized patients with CAP based on machine learning algorithms.</p><p><strong>Methods: </strong>We trained and externally validated 5 machine learning algorithms, including logistic regression, support vector machine, random forest, extreme gradient boosting, and deep forest (DF). Feature selection was conducted using the sliding window forward feature selection technique. Shapley additive explanations and local interpretable model-agnostic explanation techniques were applied to the optimal model for visual interpretation.</p><p><strong>Results: </strong>A total of 6371 patients with CAP met the inclusion criteria. The development of CAP-associated AKI (CAP-AKI) was recognized in 1006 (15.8%) patients. The 11 selected indicators were sex, temperature, breathing rate, diastolic blood pressure, C-reactive protein, albumin, white blood cell, hemoglobin, platelet, blood urea nitrogen, and neutrophil count. The DF model achieved the best area under the receiver operating characteristic curve (AUC) and accuracy in the internal (AUC=0.89, accuracy=0.90) and external validation sets (AUC=0.87, accuracy=0.83). Furthermore, the DF model had the best calibration among all models. In addition, a web-based prediction platform was developed to predict CAP-AKI.</p><p><strong>Conclusions: </strong>The model described in this study is the first multicenter-validated AKI prediction model that accurately predicts CAP-AKI during hospitalization. The web-based prediction platform embedded with the DF model serves as a user-friendly tool for early identification of high-risk patients.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e51255"},"PeriodicalIF":5.8,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142854138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methodological Challenges in Randomized Controlled Trials of mHealth Interventions: Cross-Sectional Survey Study and Consensus-Based Recommendations.","authors":"Jesus Lopez-Alcalde, L Susan Wieland, Yuqian Yan, Jürgen Barth, Mohammad Reza Khami, Siddharudha Shivalli, Cynthia Lokker, Harleen Kaur Rai, Paul Macharia, Sergi Yun, Elvira Lang, Agnes Bwanika Naggirinya, Concepción Campos-Asensio, Leila Ahmadian, Claudia M Witt","doi":"10.2196/53187","DOIUrl":"https://doi.org/10.2196/53187","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Mobile health (mHealth) refers to using mobile communication devices such as smartphones to support health, health care, and public health. mHealth interventions have their own nature and characteristics that distinguish them from traditional health care interventions, including drug interventions. Thus, randomized controlled trials (RCTs) of mHealth interventions present specific methodological challenges. Identifying and overcoming those challenges is essential to determine whether mHealth interventions improve health outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We aimed to identify specific methodological challenges in RCTs testing mHealth interventions' effects and develop consensus-based recommendations to address selected challenges.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A 2-phase participatory research project was conducted. First, we sent a web-based survey to authors of mHealth RCTs. Survey respondents rated on a 5-point scale how challenging they found 21 methodological aspects in mHealth RCTs compared to non-mHealth RCTs. Nonsystematic searches until June 2022 informed the selection of the methodological challenges listed in the survey. Second, a subset of survey respondents participated in an online workshop to discuss recommendations to address selected methodological aspects identified in the survey. Finally, consensus-based recommendations were developed based on the workshop discussion and email interaction.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We contacted 1535 authors of mHealth intervention RCTs, of whom 80 (5.21%) completed the survey. Most respondents (74/80, 92%) identified at least one methodological aspect as more or much more challenging in mHealth RCTs. The aspects most frequently reported as more or much more challenging were those related to mHealth intervention integrity, that is, the degree to which the study intervention was implemented as intended, in particular managing low adherence to the mHealth intervention (43/77, 56%), defining adherence (39/79, 49%), measuring adherence (33/78, 42%), and determining which mHealth intervention components are used or received by the participant (31/75, 41%). Other challenges were also frequent, such as analyzing passive data (eg, data collected from smartphone sensors; 24/58, 41%) and verifying the participants' identity during recruitment (28/68, 41%). In total, 11 survey respondents participated in the subsequent workshop (n=8, 73% had been involved in at least 2 mHealth RCTs). We developed 17 consensus-based recommendations related to the following four categories: (1) how to measure adherence to the mHealth intervention (7 recommendations), (2) defining adequate adherence (2 recommendations), (3) dealing with low adherence rates (3 recommendations), and (4) addressing mHealth intervention components (5 recommendations).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;RCTs of mHealth interventions have specific methodological challenges compared to those of non-mHe","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e53187"},"PeriodicalIF":5.8,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142864035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dimensions and Subcategories of Digital Maturity in General Practice: Qualitative Study.","authors":"Timo Neunaber, Achim Mortsiefer, Sven Meister","doi":"10.2196/57786","DOIUrl":"10.2196/57786","url":null,"abstract":"<p><strong>Background: </strong>The status of the digitalization of companies and institutions is usually measured using maturity models. However, the concept of maturity in general practice is currently unclear, and herewith we examine the question of how maturity can be measured. There is a lack of empirical work on the dimensions and subcategories of digital maturity that provide information on the assessment framework.</p><p><strong>Objective: </strong>The aim of the study was to answer the question of how many and which dimensions and subcategories describe digital maturity in general practice.</p><p><strong>Methods: </strong>An explorative, qualitative research design based on semistructured expert interviews was used to investigate the dimensions of digital maturity. Twenty experts from various areas of the health care sector (care providers, interest groups, health care industry, and patient organizations) were interviewed. The interviews were analyzed based on a content-structuring analysis according to Kuckartz and Rädiker using MAXQDA software (VERBI GmbH).</p><p><strong>Results: </strong>In total, 6 dimensions with a total of 26 subcategories were identified. Of these, 4 dimensions with a total of 16 subcategories (1) digitally supported processes, (2) practice staff, (3) organizational structures and rules, and (4) technical infrastructure and were deductively linked to digital maturity. In addition to the use of digital solutions, digital maturity included, for example, individual, organizational, and technical capabilities and resources of the medical practice. The 2 further dimensions, (5) benefits and outcomes and (6) external framework conditions of the medical practice, were identified inductively with a total of 10 subcategories. Digital maturity was associated with the beneficial use of digitalization, for example, with efficiency benefits for the practice, and external framework conditions were associated with influencing factors such as the local patient situation in the medical practice.</p><p><strong>Conclusions: </strong>The results indicate that digital maturity is a multidimensional construct that is associated with many dimensions and variables. It is a holistic approach with human, organizational, and technical factors and concerns the way digitalization is used to shape patient care and processes. Furthermore, it is related to the maturity of the organizational environment as well as the benefits of a digitalized medical practice; however, this still needs to be confirmed. To measure the level of digital maturity in outpatient care as accurately as possible, maturity models should therefore be multilayered and take external influencing factors into account. Future research should statistically validate the identified dimensions. At the same time, correlations and dependencies between the measurement dimensions and their subcategories should be analyzed.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e57786"},"PeriodicalIF":5.8,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142854264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When and Why Adults Abandon Lifestyle Behavior and Mental Health Mobile Apps: Scoping Review.","authors":"Patrick G Kidman, Rachel G Curtis, Amanda Watson, Carol A Maher","doi":"10.2196/56897","DOIUrl":"https://doi.org/10.2196/56897","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;With 1 in 3 adults globally living with chronic conditions and the rise in smartphone ownership, mobile health apps have become a prominent tool for managing lifestyle-related health behaviors and mental health. However, high rates of app abandonment pose challenges to their effectiveness.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We explored the abandonment of apps used for managing physical activity, diet, alcohol, smoking, and mental health in free-living conditions, examining the duration of app use before abandonment and the underlying reasons.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A scoping review was conducted based on the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines and eligibility criteria were designed according to the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research type) framework. In total, 4 databases were searched (MEDLINE, Scopus, Embase, and PsycINFO) to identify quantitative and qualitative studies with outcome measures related to app abandonment in adults with free-living conditions, including reasons for abandonment and duration of use, for mobile apps related to WHO (World Health Organization) modifiable health behaviors and mental health. The included studies' risk of bias was appraised based on the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) and COREQ (Consolidated Criteria for Reporting Qualitative Research) checklists. To enable data synthesis across different methodologies, app domains, demographic data, and outcome measures were categorized. Results are presented in 2 sections: quantitatively in a scatterplot to understand when users abandon apps and qualitatively through basic qualitative content analysis to identify the underlying reasons.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Eighteen eligible studies (525,824 participants) published between 2014 and 2022, predominantly from the United States, Canada, the United Kingdom, and Germany, were identified. Findings revealed a curvilinear pattern of app abandonment, with sharper abandonment soon after acquisition, followed by a slowing rate of abandonment over time. Taken together, a median of 70% of users discontinued use within the first 100 days. The abandonment rate appeared to vary by app domain, with apps focusing on alcohol and smoking exhibiting faster abandonment, and physical activity and mental health exhibiting longer usage durations. In total, 22 unique reasons for abandonment were organized into six categories: (1) technical and functional issues, (2) privacy concerns, (3) poor user experience, (4) content and features, (5) time and financial costs, and (6) evolving user needs and goals.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study highlights the complex nature of health app abandonment and the need for an improved understanding of user engagement over time, underscoring the importance of addressing various factors con","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e56897"},"PeriodicalIF":5.8,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142854212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Participant Recruitment in Clinical Trials: Comparative Analysis of Innovative Digital Platforms.","authors":"Alexia Georgia Bikou, Elena Deligianni, Foteini Dermiki-Gkana, Nikolaos Liappas, José Gabriel Teriús-Padrón, Maria Eugenia Beltrán Jaunsarás, Maria Fernanda Cabrera-Umpiérrez, Christos Kontogiorgis","doi":"10.2196/60504","DOIUrl":"https://doi.org/10.2196/60504","url":null,"abstract":"<p><strong>Background: </strong>Pharmaceutical product development relies on thorough and costly clinical trials. Participant recruitment and monitoring can be challenging. The incorporation of cutting-edge technologies such as blockchain and artificial intelligence has revolutionized clinical research (particularly in the recruitment stage), enhanced secure data storage and analysis, and facilitated participant monitoring while protecting their personal information.</p><p><strong>Objective: </strong>This study aims to investigate the use of novel digital platforms and their features, such as e-recruitment, e-consent, and matching, aiming to optimize and expedite clinical research.</p><p><strong>Methods: </strong>A review with a systematic approach was conducted encompassing literature from January 2000 to October 2024. The MEDLINE, ScienceDirect, Scopus, and Google Scholar databases were examined thoroughly using a customized search string. Inclusion criteria focused on digital platforms involving clinical trial recruitment phases that were in English and had international presence, scientific validation, regulatory approval, and no geographic limitations. Literature reviews and unvalidated digital platforms were excluded. The selected studies underwent meticulous screening by the research team, ensuring a thorough analysis of novel digital platforms and their use and features for clinical trials.</p><p><strong>Results: </strong>A total of 24 digital platforms were identified that supported clinical trial recruitment phases. In general, most of them (n=22, 80%) are headquartered and operating in the United States, providing a range of functionalities including electronic consent (n=14, 60% of the platforms), participant matching, and monitoring of patients' health status. These supplementary features enhance the overall effectiveness of the platforms in facilitating the recruitment process for clinical trials. The analysis and digital platform findings refer to a specific time frame when the investigation took place, and a notable surge was observed in the adoption of these novel digital tools, particularly following the COVID-19 outbreak.</p><p><strong>Conclusions: </strong>This study underscores the vital role of the identified digital platforms in clinical trials, aiding in recruitment, enhancing patient engagement, accelerating procedures, and personalizing vital sign monitoring. Despite their impact, challenges in accessibility, compatibility, and transparency require careful consideration. Addressing these challenges is crucial for optimizing digital tool integration into clinical research, allowing researchers to harness the benefits while managing the associated risks effectively.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e60504"},"PeriodicalIF":5.8,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142854272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness and Safety of the TRIO Optimal Health Management Program in Patients with Type 2 Diabetes Mellitus (T2DM) Initiating Basal Insulin Therapy: Real-World Study.","authors":"Chenxi Li, Lixin Guo, Lixin Shi, Li Chen, Liming Chen, Yaoming Xue, Hong Li, Yuzhen Liang, Jing Yang, Weimin Wang, Dalong Zhu","doi":"10.2196/67554","DOIUrl":"https://doi.org/10.2196/67554","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Diabetes, a chronic disease necessitating long-term treatment and self-management, presents significant challenges for patients who spend most of their treatment time outside of hospitals. The potential of digital therapeutics for diabetes has garnered recognition from different organizations. Although some prior studies have demonstrated successful reductions in patients' blood glucose levels and body weight through digital diabetes programs, many studies were limited by including prediabetes patients, patients treated with mostly premixed insulin, or evaluating user engagement outcomes rather than clinical outcomes. Consequently, limited evidence remains regarding the effectiveness of health management mobile applications specifically designed for T2DM patients initiating BI (basal insulin). Based on this, a data-based and artificial intelligence management system named TRIO was developed to provide patients with more personalized intervention methods in stages, groups, and around the clock. TRIO assists doctors and nurses in achieving better blood glucose controls, truly carries out standardized management around patients, and allows them to have a higher quality of life. TRIO represents the three essential pillars in comprehensive diabetes management: physician, nurse, and patient.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This prospective observational study evaluated the effectiveness and safety of the TRIO optimal health management program for patients with type 2 diabetes mellitus (T2DM) initiating basal insulin therapy in a real-world setting.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Patients aged 18 to 85 with inadequate glycemic control (baseline HbA1c ≥7.0%) starting basal insulin therapy were enrolled in outpatient and inpatient settings. The study was 3 months, with health education and phone-based follow-up assessments. Data collected included patient characteristics, medical history, baseline diabetes conditions, treatment compliance, glycemic control, and safety indicators.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 199,431 patients were included, and 118,134 patients completed the 3-month follow-up between Dec 1, 2019, and Dec 31, 2021, involving 574 hospitals in China. The mean baseline HbA1c was 9.2%, the mean duration of diabetes was 7.3 years, and 80.4% of patients were using basal insulin with oral antihyperglycemic drugs. After the intervention, mean HbA1c decreased by -2.59% from baseline, with 55.6% achieving the target HbA1c level of &lt;7.0%. Patients who set lower fasting plasma glucose (FPG) goals (&lt;6.1 mmol/L) showed more significant HbA1c reductions and higher target achievement compared to those with FPG goals≥6.1 mmol/L. Factors such as complications, diabetes duration, and baseline HbA1c levels influenced the magnitude of HbA1c reduction. The presence of complications, shorter diabetes duration, and higher baseline HbA1c were significantly associated with increased hypoglycemia incidence risk.&lt;/p&gt;&lt;p&gt;","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":" ","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142854116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Digital Approach for Addressing Suicidal Ideation and Behaviors in Youth Mental Health Services: Observational Study.","authors":"Min K Chong, Ian B Hickie, Antonia Ottavio, David Rogers, Gina Dimitropoulos, Haley M LaMonica, Luke J Borgnolo, Sarah McKenna, Elizabeth M Scott, Frank Iorfino","doi":"10.2196/60879","DOIUrl":"10.2196/60879","url":null,"abstract":"<p><strong>Background: </strong>Long wait times for mental health treatments may cause delays in early detection and management of suicidal ideation and behaviors, which are crucial for effective mental health care and suicide prevention. The use of digital technology is a potential solution for prompt identification of youth with high suicidality.</p><p><strong>Objective: </strong>The primary aim of this study was to evaluate the use of a digital suicidality notification system designed to detect and respond to suicidal needs in youth mental health services. Second, the study aimed to characterize young people at different levels of suicidal ideation and behaviors.</p><p><strong>Methods: </strong>Young people aged between 16 and 25 years completed multidimensional assessments using a digital platform, collecting demographic, clinical, social, functional, and suicidality information. When the suicidality score exceeded a predetermined threshold, established based on clinical expertise and service policies, a rule-based algorithm configured within the platform immediately generated an alert for treating clinicians. Subsequent clinical actions and response times were analyzed.</p><p><strong>Results: </strong>A total of 2021 individuals participated, of whom 266 (11%) triggered one or more high suicidal ideation and behaviors notification. Of the 292 notifications generated, 76% (222/292) were resolved, with a median response time of 1.9 (range 0-50.8) days. Clinical actions initiated to address suicidality included creating safety plans (60%, 134/222), conducting safety checks (18%, 39/222), psychological therapy (8%, 17/222), transfer to another service (3%, 8/222), and scheduling of new appointments (2%, 4/222). Young people with high levels of suicidality were more likely to present with more severe and comorbid symptoms, including low engagement in work or education, heterogenous psychopathology, substance misuse, and recurrent illness.</p><p><strong>Conclusions: </strong>The digital suicidality notification system facilitated prompt clinical actions by alerting clinicians to high levels of suicidal ideation and behaviors detected among youth. Further, the multidimensional assessment revealed complex and comorbid symptoms exhibited in youth with high suicidality. By expediting and personalizing care for those displaying elevated suicidality, the digital notification system can play a pivotal role in preventing rapid symptom progression and its detrimental impacts on young people's mental health.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e60879"},"PeriodicalIF":5.8,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142846804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"User Experience and Extended Technology Acceptance Model in Commercial Health Care App Usage Among Patients With Cancer: Mixed Methods Study.","authors":"Ye-Eun Park, Yae Won Tak, Inhye Kim, Hui Jeong Lee, Jung Bok Lee, Jong Won Lee, Yura Lee","doi":"10.2196/55176","DOIUrl":"https://doi.org/10.2196/55176","url":null,"abstract":"<p><strong>Background: </strong>The shift in medical care toward prediction and prevention has led to the emergence of digital health care as a valuable tool for managing health issues. Aiding long-term follow-up care for cancer survivors and contributing to improved survival rates. However, potential barriers to mobile health usage, including age-related disparities and challenges in user retention for commercial health apps, highlight the need to assess the impact of patients' abilities and health status on the adoption of these interventions.</p><p><strong>Objective: </strong>This study aims to investigate the app adherence and user experience of commercial health care apps among cancer survivors using an extended technology acceptance model (TAM).</p><p><strong>Methods: </strong>The study enrolled 264 cancer survivors. We collected survey results from May to August 2022 and app usage records from the app companies. The survey questions were created based on the TAM.</p><p><strong>Results: </strong>We categorized 264 participants into 3 clusters based on their app usage behavior: short use (n=77), medium use (n=101), and long use (n=86). The mean usage days were 9 (SD 11) days, 58 (SD 20) days, and 84 (SD 176) days, respectively. Analysis revealed significant differences in perceived usefulness (P=.01), interface satisfaction (P<.01), equity (P<.01), and utility (P=.01) among the clusters. Structural equation modeling indicated that perceived ease-of-use significantly influenced perceived usefulness (β=0.387, P<.01), and both perceived usefulness and attitude significantly affected behavioral intention and actual usage.</p><p><strong>Conclusions: </strong>This study showed the importance of positive user experience and clinician recommendations in facilitating the effective usage of digital health care tools among cancer survivors and contributing to the evolving landscape of medical care.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e55176"},"PeriodicalIF":5.8,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142854280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prevention and Health Promotion Interventions for Young People in the Context of Digital Well-Being: Rapid Systematic Review.","authors":"Michelle Colder Carras, Dahlia Aljuboori, Jing Shi, Mayank Date, Fatima Karkoub, Karla García Ortiz, Fasika Molla Abreha, Johannes Thrul","doi":"10.2196/59968","DOIUrl":"10.2196/59968","url":null,"abstract":"<p><strong>Background: </strong>Increasing digital technology and media use among young people has raised concerns about problematic use and negative consequences. The formal recognition of a technology addiction (eg, gaming disorder) requires an understanding of the landscape of interventions designed to prevent this disorder and related technology addictions.</p><p><strong>Objective: </strong>We conducted a rapid systematic review to investigate the current evidence on approaches to prevent problematic technology use and promote digital well-being, defined as the healthy use of digital media and technology and the absence of problems resulting from excessive use.</p><p><strong>Methods: </strong>We used a pragmatic and rapid approach to systematically review and synthesize recent literature with a focus on contextual factors that can aid in understanding translatability, making trade-offs appropriate for rapid reviews per the Cochrane Collaboration guidelines. We searched multiple databases, including gray literature, for primary studies and systematic reviews of prevention interventions targeting children, adolescents, and youth. We extracted data on study characteristics, quality, and translatability and synthesized evidence through narrative description and vote counting of controlled trials. Data are openly available on our Open Science Framework website.</p><p><strong>Results: </strong>We found 6416 citations, of which 41 (0.64%) were eligible for inclusion (6 reviews and 35 primary studies of 33 interventions). Most interventions (26/33, 79%) combined intervention approaches and included an education component. Synthesis through vote counting showed benefits for all forms of digital well-being. Both included meta-analyses reported small positive effects on reductions of screen time. However, study reporting was overall lacking, impairing the ability to draw conclusions.</p><p><strong>Conclusions: </strong>As digital technology use increases, interventions to prevent problematic technology use and promote digital well-being continue to proliferate. Understanding context factors that influence healthy technology use and understanding the limitations of the current evidence are vital for informing future research. This review demonstrates positive findings for the effectiveness of prevention interventions and describes factors that may contribute to translation and implementation. Future research would benefit from following appropriate reporting guidelines, reporting both the benefits and harms of interventions, and including greater detail on factors informing translation.</p><p><strong>Trial registration: </strong>PROSPERO CRD42023444387; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=444387.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e59968"},"PeriodicalIF":5.8,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142846857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}