UNASSIGNED: The objective of this study was to investigate the association between demographic, clinical, and interface factors and noninvasive ventilation (NIV) usage.
UNASSIGNED: A retrospective cohort analysis of 478 patients prescribed NIV from 2013 to 2021 was performed. Demographic factors, clinical indications for NIV, and interface factors were collected, and linear regression was conducted to evaluate the association between these variables and NIV usage (hour/night).
UNASSIGNED: The average usage of the cohort was 6.5 hour/night ± 4.6, with an average age of 57 years ± 16 and body mass index (BMI) of 40.5kg/m2 ± 14.7. The cohort was mostly male (n = 290, 60.6%). The most common indications for NIV prescription were high-pressure requirement for obstructive sleep apnea (HPR, n = 190, 39.7%), neuromuscular disease (NMD, n = 140, 29.3%), and obesity hypoventilation syndrome (OHS, n = 111, 23.2%). A diagnosis of NMD was a significant predictor of higher NIV usage (8.0 ± 6.1 hour/night) in multivariate analysis (p = .036). The HPR subcohort had the lowest usage of all indications. Age and BMI did not predict usage. A nasal interface (p < .01) and lower expiratory positive airway pressure (EPAP) setting (p < .001) were associated with increased NIV usage.
UNASSIGNED: This study highlights the multifaceted nature of NIV usage. Where demographic factors were not consistent predictors of usage, interface, and clinical indication were associated with usage. These findings highlight that the HPR users are a group at risk of low usage.

BACKGROUND: The widespread use of online social networks, particularly among the younger demographic, has catalyzed a growing interest in exploring their influence on users\' psychological well-being. Instagram (Meta), a visually oriented platform, has garnered significant attention. Prior research has consistently indicated that Instagram usage correlates with heightened levels of perfectionism, body dissatisfaction, and diminished self-esteem. Perfectionism is closely linked to self-criticism, which entails an intense self-scrutiny and is often associated with various psychopathologies. Conversely, self-compassion has been linked to reduced levels of perfectionism and stress, while fostering greater positive affect and overall life satisfaction.
OBJECTIVE: This study investigates the relationship between Instagram usage (time of use and content exposure) and users\' levels of self-compassion, self-criticism, and body dissatisfaction.
METHODS: This study comprised 1051 adult participants aged between 18 and 50 years, either native to Spain or residing in the country for at least a decade. Each participant completed a tailored questionnaire on Instagram usage, along with abbreviated versions of the Self-Compassion Scale, the Body Shape Questionnaire, and the Depressive Experiences Questionnaire, spanning from January 23 to February 25, 2022.
RESULTS: A positive correlation was observed between daily Instagram usage and self-criticism scores. Participants of all age groups who spent over 3 hours per day on Instagram exhibited higher self-criticism scores than users who spent less than 1 hour or between 1 and 3 hours per day. Contrary to previous findings, no significant relationship was detected between Instagram usage time and levels of self-compassion or body dissatisfaction. Furthermore, content centered around physical appearance exhibited a positive correlation with self-criticism and body dissatisfaction scores. Among younger participants (aged 18-35 years), those who primarily viewed beauty or fashion content reported higher self-criticism scores than those consuming science-related content. However, this association was not significant for participants aged 35-50 years. Conversely, individuals who predominantly engaged with sports or fitness or family or friends content exhibited higher levels of body dissatisfaction than those focusing on science-related content. No significant associations were observed between self-compassion scores and daily Instagram usage or most-viewed content categories.
CONCLUSIONS: The findings of this study underscore the considerable impact of Instagram usage on self-criticism and body dissatisfaction-2 variables known to influence users\' psychological well-being and be associated with various symptoms and psychological disorders.

BACKGROUND: Demographic changes and a low birth rate have led to a workforce shortage in Japan. To address this issue, the government has promoted engagement of female employment. However, increased female employment can impact women\'s health. Using Internet of Things (IoT) and apps to manage women\'s health has gained attention, but few studies have focused on working women.
OBJECTIVE: This study aimed to clarify the current situation of working women and their use of IoT or apps to manage their health.
METHODS: A large-scale, nationwide internet survey was conducted among 10,000 female participants aged from 20 years to 64 years in Japan. Participants were recruited from a marketing research company\'s active survey panel of 5.24 million members. The survey included questions about health status, sociodemographic factors, psychological characteristics, and the use of IoT or apps for health management. We compared perceived health status and reasons for current IoT use using t tests and assessed participant characteristics that predicted IoT use using the C5.0 decision tree algorithm. Ethical approval was granted by St. Luke\'s International University.
RESULTS: Among participants, 14.6% (1455/10,000) currently used IoT or apps, 7% (695/10,000) used them previously, and 78.5% (7850/10,000) had never used them. Current users (42.7 years old) were older than past users (39.7 years old). Discrepancies were observed between participants\' perceived health problems and the purpose for using IoT or apps, with 21.3% (2130/10,000) of all women reporting they experienced menstrual symptoms or disorders but only 3.5% (347/10,000) used IoT or apps to manage the same symptom. On the other hand, current users were more likely to use IoT or apps to manage nutrition-related problems such as underweight or obesity (405/1455, 27.8%). Device use was highest among current users, with 87.3% (1270/1455) using smartphones, 19.7% (287/1455) using smartwatches, and 13.3% (194/1455) using PCs. Decision tree analysis identified 6 clusters, the largest consisting of 81.6% (5323/6523) of non-IoT users who did not exercise regularly, while pregnant women were more likely to use IoT or apps.
CONCLUSIONS: Our findings highlight the idea that woman with particular health problems (ie, menstrual symptoms or disorders and premenstrual syndrome) have lower use of IoT or apps, suggesting an unmet need for IoT and apps in specific areas.

UNASSIGNED: The Hispanic community represents a sizeable community that experiences inequities in the US health care system. As the system has moved toward digital health platforms, evaluating the potential impact on Hispanic communities is critical.
UNASSIGNED: The study aimed to investigate demographic, socioeconomic, and behavioral factors contributing to low telehealth use in Hispanic communities.
UNASSIGNED: We used a retrospective observation study design to examine the study objectives. The COVID-19 Research Database Consortium provided the Analytics IQ PeopleCore consumer data and Office Alley claims data. The study period was from March 2020 to April 2021. Multiple logistic regression was used to determine the odds of using telehealth services.
UNASSIGNED: We examined 3,478,287 unique Hispanic patients, 16.6% (577,396) of whom used telehealth. Results suggested that patients aged between 18 and 44 years were more likely to use telehealth (odds ratio [OR] 1.07, 95% CI 1.05-1.1; P<.001) than patients aged older than 65 years. Across all age groups, patients with high incomes were at least 20% more likely to use telehealth than patients with lower incomes (P<.001); patients who had a primary care physician (P=.01), exhibited high medical usage (P<.001), or were interested in exercise (P=.03) were more likely to use telehealth; patients who had unhealthy behaviors such as smoking and alcohol consumption were less likely to use telehealth (P<.001). Male patients were less likely than female patients to use telehealth among patients aged 65 years and older (OR 0.94, 95% CI 0.93-0.95; P<.001), while male patients aged between 18 and 44 years were more likely to use telehealth (OR 1.05, 95% CI 1.03-1.07; P<.001). Among patients younger than 65 years, full-time employment was positively associated with telehealth use (P<.001). Patients aged between 18 and 44 years with high school or less education were 2% less likely to use telehealth (OR 0.98, 95% CI 0.97-0.99; P=.005). Results also revealed a positive association with using WebMD (WebMD LLC) among patients aged older than 44 years (P<.001), while there was a negative association with electronic prescriptions among those who were aged between 18 and 44 years (P=.009) and aged between 45 and 64 years (P=.004).
UNASSIGNED: This study demonstrates that telehealth use among Hispanic communities is dependent upon factors such as age, gender, education, socioeconomic status, current health care engagement, and health behaviors. To address these challenges, we advocate for interdisciplinary approaches that involve medical professionals, insurance providers, and community-based services actively engaging with Hispanic communities and promoting telehealth use. We propose the following recommendations: enhance access to health insurance, improve access to primary care providers, and allocate fiscal and educational resources to support telehealth use. As telehealth increasingly shapes health care delivery, it is vital for professionals to facilitate the use of all available avenues for accessing care.

BACKGROUND: Self-management is endorsed in clinical practice guidelines for the care of musculoskeletal pain. In a randomized clinical trial, we tested the effectiveness of an artificial intelligence-based self-management app (selfBACK) as an adjunct to usual care for patients with low back and neck pain referred to specialist care.
OBJECTIVE: This study is a process evaluation aiming to explore patients\' engagement and experiences with the selfBACK app and specialist health care practitioners\' views on adopting digital self-management tools in their clinical practice.
METHODS: App usage analytics in the first 12 weeks were used to explore patients\' engagement with the SELFBACK app. Among the 99 patients allocated to the SELFBACK interventions, a purposive sample of 11 patients (aged 27-75 years, 8 female) was selected for semistructured individual interviews based on app usage. Two focus group interviews were conducted with specialist health care practitioners (n=9). Interviews were analyzed using thematic analysis.
RESULTS: Nearly one-third of patients never accessed the app, and one-third were low users. Three themes were identified from interviews with patients and health care practitioners: (1) overall impression of the app, where patients discussed the interface and content of the app, reported on usability issues, and described their app usage; (2) perceived value of the app, where patients and health care practitioners described the primary value of the app and its potential to supplement usual care; and (3) suggestions for future use, where patients and health care practitioners addressed aspects they believed would determine acceptance.
CONCLUSIONS: Although the app\'s uptake was relatively low, both patients and health care practitioners had a positive opinion about adopting an app-based self-management intervention for low back and neck pain as an add-on to usual care. Both described that the app could reassure patients by providing trustworthy information, thus empowering them to take actions on their own. Factors influencing app acceptance and engagement, such as content relevance, tailoring, trust, and usability properties, were identified.
BACKGROUND: ClinicalTrials.gov NCT04463043; https://clinicaltrials.gov/study/NCT04463043.

UNASSIGNED: Malaria prevention is important among HIV patients to mitigate its associated complications. This study explored the predictors of insecticide-treated net (ITN) usage among HIV patients attending an antiretroviral therapy (ART) clinic in Ebonyi state, Nigeria.
UNASSIGNED: It was a cross-sectional study that involved 297 patients who attended an ART clinic in Ebonyi State. An interviewer-administered questionnaire was used to collect relevant data which included perception of malaria prevention, ownership of ITN, condition of ITN, duration of use and ITN usage. The relationship between ITN usage and other variables was assessed using bivariate analysis and the predictors were determined using multivariate logistic regression.
UNASSIGNED: There were 64 (21.5%) males and 233(78.5%) females. Knowledge of the use of ITN (mean= 4.24 ±0.93) was rated better than knowledge of the use of mosquito repellent (mean = 3.79 ± 1.21) and knowledge of the use of protective clothes (mean= 3.86 ±1.26). 246 (83%) of the study population owned ITN. Only 48% (118) of those who had ITNs reported having optimal nets (nets without holes). 135 (45.5%) of the participants reported sleeping under the net every day in the past week. There was a significant relationship between ITN usage and knowledge of the use of ITN, ownership of ITN, duration of use and condition of nets. Predictors of usage of ITN were duration of use (AOR=0.386, 95%C.I for AOR=0.224-0.665) and condition of the nets (AOR=0.399, 95%C.I for AOR=0.226-0.706).
UNASSIGNED: Perception of malaria prevention was high among the study group. Ownership of ITN was high although many of the ITNs were not in good condition. Usage of ITN was largely determined by duration of use and good condition of nets. Yearly assessment of the condition of ITNs and replacement exercise of ITNs (2-3 years) at ART clinics to replace old and worn-out nets among HIV patients are recommended.

BACKGROUND: The utilization of artificial intelligence (AI) technologies in the biomedical field has attracted increasing attention in recent decades. Studying how past AI technologies have found their way into medicine over time can help to predict which current (and future) AI technologies have the potential to be utilized in medicine in the coming years, thereby providing a helpful reference for future research directions.
OBJECTIVE: The aim of this study was to predict the future trend of AI technologies used in different biomedical domains based on past trends of related technologies and biomedical domains.
METHODS: We collected a large corpus of articles from the PubMed database pertaining to the intersection of AI and biomedicine. Initially, we attempted to use regression on the extracted keywords alone; however, we found that this approach did not provide sufficient information. Therefore, we propose a method called \"background-enhanced prediction\" to expand the knowledge utilized by the regression algorithm by incorporating both the keywords and their surrounding context. This method of data construction resulted in improved performance across the six regression models evaluated. Our findings were confirmed through experiments on recurrent prediction and forecasting.
RESULTS: In our analysis using background information for prediction, we found that a window size of 3 yielded the best results, outperforming the use of keywords alone. Furthermore, utilizing data only prior to 2017, our regression projections for the period of 2017-2021 exhibited a high coefficient of determination (R2), which reached up to 0.78, demonstrating the effectiveness of our method in predicting long-term trends. Based on the prediction, studies related to proteins and tumors will be pushed out of the top 20 and become replaced by early diagnostics, tomography, and other detection technologies. These are certain areas that are well-suited to incorporate AI technology. Deep learning, machine learning, and neural networks continue to be the dominant AI technologies in biomedical applications. Generative adversarial networks represent an emerging technology with a strong growth trend.
CONCLUSIONS: In this study, we explored AI trends in the biomedical field and developed a predictive model to forecast future trends. Our findings were confirmed through experiments on current trends.

BACKGROUND: YouTube is a widely used video sharing and social networking platform among children and adolescents. However, research on YouTube usage among this population remains scarce. Specifically, studies on factors that influence children and adolescents\' usage are clinically significant but largely lacking. Additionally, few studies have examined the association between usage and emotional/behavioral problems, which is fundamental to smartphone research. Therefore, this study explored the relationship between early childhood temperament, subsequent YouTube usage patterns, and emotional/behavioral problems.
METHODS: The Kids Cohort for Understanding Internet Addiction Risk Factors in Early Childhood (K-CURE) is the first long-term prospective cohort study in Korea aimed at understanding the long-term effects of media exposure on young children. The study included 195 children aged 8-11 years enrolled in the K-CURE study. Caregivers, predominantly mothers, who voluntarily participated during their visits to community centers for children\'s mental health in Korea\'s major cities, completed a detailed self-administered survey. Childhood temperament was measured in 2018 when the children were 5-8 years old. Subsequent YouTube usage patterns and emotional/behavioral problems were assessed in 2021. Data were analyzed using frequency analysis, correlation analysis, and multiple linear regression.
RESULTS: The study found that 21.0% of children started using YouTube before age 4, with the most common onset age being 8-9 years (30.3%). These children used YouTube on average 4.8 days per week for 68.5 min per day. Early childhood persistence was negatively associated with the subsequent YouTube usage duration, and the age at first YouTube use was negatively correlated with subsequent usage frequency. Furthermore, a younger age at first YouTube use and higher usage frequency were significantly associated with increased emotional/behavioral problems.
CONCLUSIONS: In the YouTube environment, where content is automatically recommended based on user preferences, traits related to usage patterns may be associated with persistence, which is linked to self-regulation. Considering the current trend where children use smartphones, contents frequently for very short durations, our findings highlight the importance of self-regulation in the media usage of children who are still developing. Additionally, our results provide fundamental information for future YouTube studies and illustrate similarities and differences between smartphone and YouTube research.

UNASSIGNED: The relationship between educational outcomes and the use of web-based clinical knowledge support systems in teaching hospitals remains unknown in Japan. A previous study on this topic could have been affected by recall bias because of the use of a self-reported questionnaire.
UNASSIGNED: We aimed to explore the relationship between the use of the Wolters Kluwer UpToDate clinical knowledge support system in teaching hospitals and residents\' General Medicine In-Training Examination (GM-ITE) scores. In this study, we objectively evaluated the relationship between the total number of UpToDate hospital use logs and the GM-ITE scores.
UNASSIGNED: This nationwide cross-sectional study included postgraduate year-1 and -2 residents who had taken the examination in the 2020 academic year. Hospital-level information was obtained from published web pages, and UpToDate hospital use logs were provided by Wolters Kluwer. We evaluated the relationship between the total number of UpToDate hospital use logs and residents\' GM-ITE scores. We analyzed 215 teaching hospitals with at least 5 GM-ITE examinees and hospital use logs from 2017 to 2019.
UNASSIGNED: The study population consisted of 3013 residents from 215 teaching hospitals with at least 5 GM-ITE examinees and web-based resource use log data from 2017 to 2019. High-use hospital residents had significantly higher GM-ITE scores than low-use hospital residents (mean 26.9, SD 2.0 vs mean 26.2, SD 2.3; P=.009; Cohen d=0.35, 95% CI 0.08-0.62). The GM-ITE scores were significantly correlated with the total number of hospital use logs (Pearson r=0.28; P<.001). The multilevel analysis revealed a positive association between the total number of logs divided by the number of hospital physicians and the GM-ITE scores (estimated coefficient=0.36, 95% CI 0.14-0.59; P=.001).
UNASSIGNED: The findings suggest that the development of residents\' clinical reasoning abilities through UpToDate is associated with high GM-ITE scores. Thus, higher use of UpToDate may lead physicians and residents in high-use hospitals to increase the implementation of evidence-based medicine, leading to high educational outcomes.

UNASSIGNED: There is growing concern around the use of sodium nitrite (SN) as an emerging means of suicide, particularly among younger people. Given the limited information on the topic from traditional public health surveillance sources, we studied posts made to an online suicide discussion forum, \"Sanctioned Suicide,\" which is a primary source of information on the use and procurement of SN.
UNASSIGNED: This study aims to determine the trends in SN purchase and use, as obtained via data mining from subscriber posts on the forum. We also aim to determine the substances and topics commonly co-occurring with SN, as well as the geographical distribution of users and sources of SN.
UNASSIGNED: We collected all publicly available from the site\'s inception in March 2018 to October 2022. Using data-driven methods, including natural language processing and machine learning, we analyzed the trends in SN mentions over time, including the locations of SN consumers and the sources from which SN is procured. We developed a transformer-based source and location classifier to determine the geographical distribution of the sources of SN.
UNASSIGNED: Posts pertaining to SN show a rise in popularity, and there were statistically significant correlations between real-life use of SN and suicidal intent when compared to data from the Centers for Disease Control and Prevention (CDC) Wide-Ranging Online Data for Epidemiologic Research (⍴=0.727; P<.001) and the National Poison Data System (⍴=0.866; P=.001). We observed frequent co-mentions of antiemetics, benzodiazepines, and acid regulators with SN. Our proposed machine learning-based source and location classifier can detect potential sources of SN with an accuracy of 72.92% and showed consumption in the United States and elsewhere.
UNASSIGNED: Vital information about SN and other emerging mechanisms of suicide can be obtained from online forums.