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Problematic social media use has been linked to reduced well-being and impulse control difficulties. While digital self-control apps show potential for reducing general app usage, they often lack customization, leading to limited effectiveness and increased user resistance. Their impact on problematic social media use remains uncertain. This study evaluates the effectiveness of the Wellspent app, a customizable mobile intervention app designed to promote self-regulated social media use by targeting user-defined problematic app use and offering tailored behavioral nudges. In a 3-week randomized controlled trial, 70 iPhone users (mean age 26.2, SD 5.6 years; 47/70, 67% female), regularly using at least 1 social media app, were randomly assigned to an intervention (n=35) or control group (n=35). The intervention group received personalized full-screen reminders with the option to quit or continue social media app use whenever an app session exceeded a self-defined time limit. Participants completed weekly online surveys measuring problematic social media use, problematic smartphone use, self-efficacy, and daily screen time on their most problematic app. Linear mixed models tested intervention effects. While no significant reduction in problematic social media use or increase in self-efficacy was observed, the intervention group showed a significant reduction in daily screen time on their most problematic app by approximately 29 minutes (estimate=-29.35, SE 6.84, 95% CI -42.79 to -15.99; P<.001), and a significant decrease in perceived problematic smartphone use (estimate=-0.46, SE 0.18, 95% CI -0.80 to -0.11; P=.01). The Wellspent app demonstrated short-term efficacy in reducing problematic smartphone use. By allowing users to tailor interventions to their personal goals, the app shows promise as a self-directed tool to support healthier digital habits. Further research should explore long-term effects and feature-specific impacts.

The research aims to investigate the effects of social media use on body dissatisfaction and stress among university students. This study used a descriptive and cross-sectional design. Data were collected online from 717 students in T&#xfc;rkiye through the Personal Information Form, the Social Media Addiction Scale, the Perceived Stress Scale, and the Body Image Scale. Of the participants, 90.8&#xa0;% were aged 18-25 and 82.3&#xa0;% were female. Younger students had higher Social Media Addiction and Perceived Stress scores, while Body Image scores increased with age. Female students scored higher on Social Media Addiction and Perceived Stress than males (p&#xa0;<&#xa0;0.010; p&#xa0;<&#xa0;0.000), with no gender difference in Body Image. Educational level was unrelated to Social Media Addiction or Perceived Stress, but Body Image scores were significantly higher among undergraduate and graduate students (p&#xa0;<&#xa0;0.000). Phone use was more frequent than computer use. Increased phone use was associated with higher Social Media Addiction and Perceived Stress (p&#xa0;<&#xa0;0.000; p&#xa0;<&#xa0;0.005), while greater computer use was associated with higher Perceived Stress and Body Image scores (p&#xa0;<&#xa0;0.005; p&#xa0;<&#xa0;0.020). Social Media Addiction was higher among TikTok, Snapchat, and multi-platform users, whereas Body Image scores were higher among Twitter and Facebook users (p&#xa0;<&#xa0;0.000). Social Media Addiction correlated positively with Perceived Stress (r&#xa0;=&#xa0;0.289, p&#xa0;<&#xa0;0.01) and negatively with Body Image (r&#xa0;=&#xa0;-0.124, p&#xa0;<&#xa0;0.01). Social media use increases addiction and stress while impairing body image among university students. These results highlight the importance of developing preventive mental health initiatives related to social media use.

BackgroundMusculoskeletal pain (MSP) is a common occupational health concern among sedentary workers, including academic staff.ObjectiveThis exploratory study assessed the prevalence, associated factors, and effects of MSP among academic staff at a public university in Saudi Arabia.MethodsA cross-sectional survey was conducted using a self-administered questionnaire incorporating the Extended Nordic Musculoskeletal Questionnaire and items assessing individual, health, and occupational characteristics. Chi-square, Fisher's exact, and logistic regression analyses were performed with an alpha level of 0.05.ResultsSeventy-one academic staff (50 females; mean age: 40.0&#x2009;&#xb1;&#x2009;6.4 years) participated. Across all assessed timeframes (lifetime, past year, past month, and point), MSP was most commonly reported in the neck (62.0%, 36.6%, 25.4%, and 11.6%, respectively), low back (61.4%, 39.4%, 32.9%, and 11.6%, respectively), and shoulders (52.9%, 34.3%, 23.2%, and 13.2%, respectively). Occupational factors, including high job demands, low job control, prolonged sitting time, extended computer use, lack of breaks, and leadership responsibilities, were most consistently associated with MSP. Health-related characteristics (smoking, comorbidities, and medication use) and individual characteristics (sex and smartphone use) showed more site-specific associations. MSP affected daily functioning, leading to medication use, work absences, and healthcare visits.ConclusionMSP was commonly reported among academic staff in this Saudi university, with occupational characteristics showing the most consistent associations. Preventive strategies, including ergonomic training, work break policies, and stress management, may be beneficial for reducing MSP within this academic setting.

BACKGROUND: Currently the use of electronic medical records (EMR) systems emerged globally especially OpenMRS to significantly improve healthcare delivery. However, challenges persist in achieving full EMR adoption, particularly in developing countries. In Rwanda, OpenMRS system was introduced in 2013 as a strategy to align digital health to the country&#x2019;s information and communication technologies (ICT)-for-development agenda. Reports from healthcare providers indicate varying EMR adoption levels in health facilities in Rwanda. This study aims to assess the current usage of EMR and identify implementation barriers in Rwandan health facilities. METHODS: A cross-sectional study was conducted in 257 Rwandan health facilities using an electronically designed questionnaire in Epi Info 7. Face-to-face interviews were held with 1074 participants, including facility representatives and staff from departments utilizing EMRs. The study covered all district hospitals and 44% of associated health facilities. Descriptive analysis was performed to assess EMR use, user knowledge, attitudes, and practices. RESULTS: A total of 257 health facilities were assessed (42 district hospitals and 215 health centers). All 42 district hospitals used EMRs, of which 35(83.3%) used OpenMRS while only 71 (33.0%) out of 215 health centers used EMRs of which about 55% used OpenMRS. Among 234 responses for non-use of EMRs, 98 (41.8%) had never used EMRs, 46 (19.6%) reported system damage, and 42 (17.9%) reported lack of internet. Reported barriers met by users included electricity and internet issues, limited computer access, and software glitches. CONCLUSION: District hospitals in Rwanda showed to have widely utilized EMRs, while health centers exhibited lower utilization rates due to system unavailability or damage. OpenMRS was prevalent in hospitals, whereas health centers used diverse health systems. Respondents preferred EMRs over paper-based methods, but challenges such as internet issues, lack of user guides, power disruptions, staff shortages, and missing features hindered EMR adoption and use. Recommendations include providing comprehensive staff training on computer use, improving infrastructure (updating EMR modules, ensuring electricity and internet availability), and providing user guides to healthcare providers to enhance ICT implementation in health facilities. TRIAL REGISTRATION: Not applicable.

To examine the association of discretionary screen time with the risk of type 2 diabetes among individuals with prediabetes, and to explore the mediating roles of sleep and the triglyceride-glucose (TyG) index. This prospective cohort study included 41&#x2009;978 participants with prediabetes from the UK Biobank. Discretionary screen time comprised television viewing and leisure computer use. Sleep was assessed using five dimensions: duration, chronotype, insomnia, snoring and daytime dozing. The TyG index was calculated as an indicator of insulin resistance. Incident type 2 diabetes was confirmed through linked hospital, primary care and death registry records. Compared with <&#x2009;2&#x2009;h/day of discretionary screen time, >&#x2009;5&#x2009;h/day was associated with a 26% higher risk of type 2 diabetes (hazard ratio (HR): 1.26, 95% confidence interval (CI): 1.12, 1.40). Restricted cubic spline analyses revealed a non-linear association between discretionary screen time and type 2 diabetes (P for nonlinear&#x2009;=&#x2009;0.006). Television viewing (HR: 1.35, 95% CI: 1.21, 1.51) and leisure computer use (HR: 1.04, 95% CI: 0.88, 1.23) showed different associations with type 2 diabetes. Exploratory mediation analyses indicated that sleep and the TyG index mediated 30.2% of the association, with 10.7% mediated through sleep and 18.1% through the TyG index. Higher discretionary screen time was associated with an increased risk of type 2 diabetes among individuals with prediabetes, partly mediated by poor sleep and insulin resistance. Reducing discretionary screen time may be a meaningful preventive strategy against type 2 diabetes, with sleep health as an important mediator.

Diabetes poses a growing global health burden. This study investigated the causal effects of lifestyle and socioeconomic factors on diabetes risk and related complications and comorbidities. We applied two-sample univariable and multivariable Mendelian randomization to assess the causal impact of 26 lifestyle and socioeconomic factors on diabetes, 7 complications, and 13 comorbidities. Genetically predicted protective factors included vigorous physical activity (odds ratio [OR], 0.98 [0.98-0.99]), computer use time (OR, 0.13 [0.05, 0.30]), carbohydrate intake (OR, 0.22 [0.15, 0.34]), short (OR, 0.04 [0.01, 0.16]) and long sleep duration (OR, 0.62 [0.47, 0.82]), moderate alcohol (OR, 0.13 [0.04, 0.50]) and caffeine (OR, 0.72 [0.64, 0.81]) consumption, education (OR, 0.25-0.67), and household income (OR, 0.52-0.65), which were associated with reduced risks of type 2 and gestational diabetes, stroke, coronary heart disease, heart failure, myocardial infarction, sleep apnea, and anxiety disorder (adjusted P-values <0.05). Conversely, genetically predicted factors, such as television watching (OR, 1.39 [1.23, 1.57]) and driving time (OR, 3.28 [1.27, 8.48]), insomnia (OR, 1.21-1.82), smoking behaviors (OR, 1.17-1.77), alcohol dependence (OR, 1.17-1.28), coffee consumption (OR, 1.01 [1.00, 1.02]), and the Townsend deprivation index (OR, 1.51-1.57), are associated with increased risks of diabetes-related outcomes (i.e., all diabetic types, neovascular glaucoma, heart failure, nonalcoholic fatty liver disease, sleep apnea, and eating disorder) (adjusted P-values <0.05). Our findings support causal roles of lifestyle and socioeconomic factors and diabetes-related outcomes, emphasizing the need for targeted public health strategies to promote healthier living and socioeconomic equity.

BACKGROUND: Simulation-based education has become a core component of contemporary health professions training, yet the role of medical simulation technicians &#x2014; professionals responsible for the technical, logistical, and operational continuity of simulation activities &#x2014; remains inconsistently defined. Clarifying the competencies and expectations associated with this role is essential for ensuring high-quality simulation delivery, supporting educational alignment, and guiding workforce development. This study aimed to identify key attributes, skills, and knowledge areas that characterize the professional profile of medical simulation technicians in Poland. METHODS: A nationwide mixed-methods study was conducted in 2020&#x2013;2021, integrating Nominal Group Technique (NGT) discussions, questionnaire development, a national survey, and structured interviews with simulation center managers. Stakeholders included technicians, academic teachers, students, and managers of medical simulation centers (MSCs). Ideas generated in NGT sessions were rated using a 1&#x2013;5 Likert scale, and highly rated items (&#x2265;&#x2009;4) formed the basis of an 88-item questionnaire distributed to all identified MSCs nationwide. Quantitative data were analyzed using the Shapiro&#x2013;Wilk test, Mann&#x2013;Whitney test, McNemar test, Chi-square test with Yates&#x2019; correction, and structural indicator difference tests, with statistical significance set at p&#x2009;<&#x2009;0.05. Qualitative data from interviews were thematically synthesized. RESULTS: Sixty-six MSC employees completed the survey. The most valued attributes were involvement (M&#x2009;=&#x2009;4.86), willingness to work (M&#x2009;=&#x2009;4.79), helpfulness and proactivity (M&#x2009;=&#x2009;4.79), trustworthiness (M&#x2009;=&#x2009;4.68), and reliability (M&#x2009;=&#x2009;4.68). Key skills included coping with technical failures (M&#x2009;=&#x2009;4.75), equipment operation (M&#x2009;=&#x2009;4.72), and computer use (M&#x2009;=&#x2009;4.63). Essential knowledge areas were facility layout (M&#x2009;=&#x2009;4.61) and familiarity with simulation scenarios (M&#x2009;=&#x2009;4.53). Managers emphasized significant training gaps in didactic knowledge (47%), IT competencies (27%), and audiovisual systems (13%). CONCLUSIONS: Effective performance of simulation technicians requires a combination of technical proficiency, contextual knowledge, and strong interpersonal attributes. The convergence of perspectives across stakeholder groups highlights the need for standardized training pathways and competency frameworks. Implementing these findings may enhance operational quality, professional recognition, and the educational impact of MSCs.

Estimating the prevalence and identifying risk factors for allergic rhinitis (AR) provides critical burden of disease data and offers opportunity to intervene in early-life preventing morbidity. We conducted a Global Asthma Network (GAN) Phase I cross-sectional study in children (6-7&#x2009;years) and adolescents (13-14&#x2009;years). Multilevel logistic regression models were fitted with random intercepts for school, center, and country, adjusting for sex and country income at the child level. Associations between symptoms and a range of lifestyle and environmental risk factors were assessed using odds ratios and corresponding 95% confidence intervals for mean individual and school exposure. Participants provided informed consent/assent, and each center was required to provide proof of ethical clearance. We analysed data from 266,182 children and adolescents across 1688 schools in 65 centers for AR symptoms. Prevalence was 8.5% in children and 13.3% in adolescents. Early-life exposures strongly associated with AR included paracetamol use (OR: 2.03; 95% CI: 1.89-2.18) and antibiotics (OR: 1.67; 95% CI: 1.56-1.78), with a stronger effect for antibiotics in low- and middle-income countries (LMICs). Farm animal exposure increased AR risk among LMIC children (OR: 1.31; 95% CI: 1.12-1.53). In adolescents, computer use (OR: 1.28; 95% CI: 1.22-1.35) and tobacco use (OR: 1.37; 95% CI: 1.29-1.46) were significant risk factors. Heavy truck traffic consistently elevated AR risk in both age groups. The prevalence of AR is stable; early-life exposures to animals increased the risk for AR in children from LMICs. Lifestyle factors and poor air quality from traffic-related pollutants increase the risk of AR.

Scores on neuropsychological assessments are typically corrected for the influences of age, education, and gender (AEG). However, other demographic factors, such as crystallized ability and race/ethnicity, independently affect test performance. As a result, standard scores systematically over- or under-classify impairment in patients whose demographic profile differs from that of the reference population. We developed a Comprehensive (C-) model scoring algorithm that added vocabulary, age&#xb2;, race/ethnicity, Latino background, a coarse socioeconomic status proxy, computer use, and daily prescription medications to the standard AEG predictor pool. The model was developed using data from 1,914 community-dwelling adults assessed with the California Cognitive Assessment Battery (CCAB; Woods et al., 2024). For each of 118 individual cognitive measures, stability-selection LASSO identified robust predictors in 300 random 80/20 splits retained at &#x2265; 80% frequency and then estimated mean coefficients and confidence intervals in 1,000 bootstrap OLS samples. Cross-sample frozen-coefficient validation was used to evaluate scoring model generalization in two subgroups: Group 1 (n = 1,033, older, first enrolled cohort) and Group 2 (n = 881, a recently recruited younger cohort). Stability selection retained a mean of 2.81 predictors per measure (range 1-6). Compared to the AEG model, the C-model approximately doubled variance explained (r&#xb2; = 0.50 vs 0.25; mean across cognitive domains r&#xb2; = 0.32 vs 0.18) and outperformed AEG in 98.8% of individual measures with non-trivial demographic signal. Racial disparities in MCI classification (the bottom-7th-percentile) were substantially reduced: Black-vs-White ratios fell from 5.6 (AEG) to 1.8 (C). Conversely, sensitivity was improved in individuals with elevated premorbid function: MCI classification ratios in low-vs-high vocabulary quartiles fell from 11.3 to 2.1. AIC favored the C-model in 88.1% of measures (mean &#x394;AIC = -167), ruling out overfitting. Frozen-coefficient validation preserved the C-model's r&#xb2; advantage in every cognitive domain. By correcting scores for race, premorbid cognitive functioning (vocabulary), and other demographic predictors, the C-model explains substantially more variance than the AEG model, reduces racial bias, and increases sensitivity to cognitive decline in high-functioning participants. C and AEG models can be used in parallel: model concordance increases diagnostic confidence, while disagreement carries diagnostic information. We developed a Comprehensive (C-) model for scoring California Cognitive Assessment Battery (CCAB) tests that supplements standard age + education + gender (AEG) demographic corrections with additional predictors including vocabulary, age&#xb2;, race/ethnicity, Latino background, socioeconomic status (SES), computer use, and daily medications.To avoid model overfitting, significant predictors were identified with stability-selection LASSO, resulting in a mean C-model retention of 2.81 (range 1-6) predictors per individual test score.In 1,914 community-dwelling adults assessed with CCAB, the C-model approximately doubled explained variance for overall performance (OMNI) scores when compared with the AEG model (r&#xb2; = 0.50 vs 0.25), and accounted for more variance than the AEG model in 98.8% of measures with non-trivial demographic signal. Cross-sample validation with two demographically distinct cohorts showed that the C-model's r&#xb2; advantage was preserved in every cognitive domain (&#x394;r&#xb2; variation < &#xb1;0.020 across fits), supporting generalizability.The C-model reduced demographic classification disparities in classifying mild cognitive impairment (MCI, bottom 7% of participants) and normalized MCI detection across different levels of premorbid cognitive reserve: Black-vs-White MCI-classification ratios fell from 5.6 (AEG) to 1.8 (C), and low-vs-high vocabulary performance ratios fell from 11.3 to 2.1.C-model scores are orthogonal to vocabulary; comparisons with premorbid crystallized intelligence are incorporated directly in the scoring algorithm, eliminating the need for post-hoc comparisons.The C-model is best used in parallel with AEG scoring: concordance between models increases diagnostic confidence, while disagreement provides additional diagnostic information.

This study was conducted to test the reliability and validity of the Nurses' Perceptions of Electronic Documentation (NPED) scale on Turkish nurses. A methodological study. In the analysis, the scale's reliability was evaluated using Cronbach's alpha coefficient, item-total score correlations, scale response bias and the test-retest method, in addition to descriptive statistics. Validity analyses were conducted through language validity, the content validity index, construct validity (Confirmatory Factor Analysis [CFA] and Exploratory Factor Analysis [EFA]) and known-groups validity. For the 11-item NPED, item-total score correlations ranged between 0.27 and 0.68 (p&#x2009;<&#x2009;0.01). EFA was conducted due to poor goodness-of-fit indices observed in the initial CFA. The EFA identified three subfactors; however, items 2 and 10 were distributed in subdimensions different from the original scale. After removing items 2 and 10, the scale demonstrated acceptable results in EFA. While &#x3b1;&#x2009;=&#x2009;0.795 in the NPED, Factor 1 (&#x3b1;&#x2009;=&#x2009;0.855), Factor 2 (&#x3b1;&#x2009;=&#x2009;0.741) and Factor 3 (&#x3b1;&#x2009;=&#x2009;0.767) measured 73.48% of the total variance. In the known-groups validity analysis, the scale successfully identified differences based on nurses' self-reported proficiency in computer use and their prior exposure to Electronic Health Records (EHRs) training, both of which were established as criteria.

Increasing clinical complexity, rising admission volumes and shorter hospital stays have intensified demands on internal medicine residents. A 2015 time and motion study at our institution showed that residents spent nearly half of their day on computer work, with frequent task-switching and limited patient contact. These findings prompted organisational reforms to redistribute workload and improve workflow. We aimed to assess how resident time allocation changed after organisational reforms. We performed a before-and-after time and motion study in the division of internal medicine in a tertiary care centre in Switzerland. Direct observations were conducted over identical periods (May-July) in 2015 (baseline, before implementation of organisational reforms) and 2018 (first assessment after full implementation of these reforms). All residents were eligible. Shifts were randomly selected and stratified by weekday, with two shifts per resident observed whenever possible. Trained observers used a standardised electronic tool to record 22 mutually exclusive activities and contextual factors. The primary outcome was time spent on administrative tasks (patient-related and non-patient-related administration, discharge summaries, information retrieval). Secondary outcomes included task-switching rate, mismatch rate (deviation from planned schedule) and shift duration. Division workload data were collected to adjust analyses. Seventy-five residents were observed over 142 shifts (1478 hours). From 2015 to 2018, mean administrative time increased from 92 to 139 minutes/day (p <0.001) and mean task-switching from 15 to 20 per hour (p <0.001), while mean mismatch rate decreased (38.8% to 31.7%, p <0.001). The mean shift duration shortened (11h38m to 10h45m, p <0.001), with mean personal time increasing (32 to 63 minutes, p <0.001). Mean bedside time declined (113 to 92 minutes, p = 0.011) and mean computer use slightly decreased (327 to 290 minutes, p = 0.009). Mean weekly admissions rose (96 to 146, p <0.001) and mean length of stay was halved (15.5 to 8.5 days, p <0.001). Results were consistent after adjustment for division workload. Targeted reforms improved schedule alignment and work-rest balance but failed to reduce administrative burden in a high-turnover environment. Local time-management interventions should be integrated with hospital-wide strategies addressing workflow complexity, interprofessional communication and task distribution. These results may inform similar initiatives in other high-pressure inpatient training settings. ISRCTN 69703381,&#xa0;https://doi.org/10.1186/ISRCTN69703381.

The increasing reliance on digital devices in modern workplaces has raised significant concerns about computer vision syndrome (CVS), particularly among professionals in screen-intensive environments, like the banking sector. Despite its widespread prevalence, limited research exists on CVS within the Bangladeshi banking sector. This study aims to estimate the prevalence of CVS among bank employees in Bangladesh and to evaluate the key demographic, occupational, and environmental risk factors associated with its occurrence. A cross-sectional study was conducted using a structured, self-administered questionnaire. The required sample size was calculated a prior based on an expected prevalence of 85.2%, a 5% margin of error, and a 95% confidence level, resulting in a target sample of 201 Bangladeshi bank employees. The presence of CVS was determined using the validated CVS-Q tool. Descriptive statistics were conducted to summarize demographic and socioeconomic characteristics of the study participants. Logistic regression analysis was performed to identify significant predictors of CVS. The prevalence of CVS among participants was 55.2%. The most reported symptoms were headache (79%), burning eyes (63%), and eye pain (52%). Logistic regression analysis revealed that increased daily computer use (odds ratios [OR]: 2.05; 95% CI: 1.07-4.83) and very bright monitor (OR: 113; 95% CI: 1.88-18,710) were significantly associated with higher odds of CVS. Conversely, larger family size (OR: 0.63; 95% CI: 0.39-0.92), higher weekly overtime (OR: 0.77; 95% CI: 0.60-0.91), and number of leave days taken for eye problems (OR: 0.03; 95% CI: 0.00-0.41) were associated with reduced odds of CVS. CVS is a prevalent occupational health concern among Bangladeshi bank employees, driven by modifiable factors such as screen brightness and prolonged computer use. Findings underscore the need for ergonomic interventions, regular eye screening, and educational measures to mitigate CVS and promote workplace well-being.

Background/Objectives: The mediating role of the diverse range of screen-based sedentary behaviors (SBs) remains understudied, particularly at younger ages. The present study examined the direct and indirect effects of parental BMI and education on ultra-processed food (UPF) consumption among preschoolers, testing the potential mediating role of screen time. Methods: The cross-sectional study sample comprised 919 kindergarten children (484 boys, 52.7%), with ages ranging from 2.2 to 6.8 years (mean: 4.7 &#xb1; 1.0 years). Screen-based sedentary behaviors (television viewing, smartphone use, tablet use, computer use, and playing electronic games) were measured by proxy-report fulfilled by parents, separately for weekdays and weekends. UPF consumption (drinks/yogurts, packaged/fast foods, and sweet/salty snacks) was assessed via 24 h recall scales. Path analysis mediation models tested direct effects of maternal/paternal BMI and education on UPF intake, and indirect effects through screen time, controlling for child age and sex. Results: Lower parental education and higher parental BMI were associated with increased mobile device use and UPF consumption (r = 0.10-0.28). Screen-based sedentary behaviors mediated the association between maternal BMI and UPF pathways (15-90% of total effects), particularly for sweet and salty snacks (50-90%). Parental education effects were also mediated by screen time (9-23% indirect effects), with paternal education showing stronger protection against packaged/fast foods. Conclusions: Mobile devices and watching television partially mediate intergenerational transmission of obesogenic dietary patterns from parental BMI/education to preschoolers' UPF consumption. Findings of the current study support family-centered interventions targeting screen-time limits and UPF exposure, mainly at the weekends, to prevent early obesity trajectories.

To investigate the prevalence and influencing factors of scoliosis among primary and secondary school students in Chongqing, China, in order to provide a theoretical basis for the prevention and control of the disease. From September to October 2022, primary and secondary school students in Chongqing were selected for scoliosis screening and questionnaire survey using a multistage sampling method. The scoliosis group and the normal group (the control group) were matched by propensity score matching (PSM) according to a ratio of 1:4. Individual characteristics were compared before and after matching, and conditional logistic regression analyses was used to analyze the influencing factors of scoliosis after matching. The detection rate of scoliosis was 0.91% among 13,605 respondents. Differences in scoliosis detection rates by gender, age, and educational stage were statistically significant (p&#xa0;<&#xa0;0.05). The results of the conditional logistic regression analysis after PSM showed that the time of outdoor activities, physical education classes, computer use, reading and writing time after school, regular adjustment of the height of desks and chairs according to the height of the students, the weight of the school bag and mattress softness were the factors influencing scoliosis. Scoliosis is affected by a combination of factors, many of which are preventable. Therefore, families, schools and relevant authorities should pay attention to it and should strengthen the development of early screening, prevention and control.

Understanding how older adults organize their daily lives is crucial for developing person-centered homecare systems. This study proposes an interpretable framework for modeling daily life and detecting behavioral anomalies using data from 18 CASAS smart homes. The dataset contains several weeks of continuous sensor recordings from residents living independently. Daily activity patterns were analyzed in 15-minute intervals using principal component analysis (PCA) to identify key temporal patterns shared by the population. For each resident, a personal baseline routine was defined as the median of their daily activity profiles over a 14-day baseline period, and deviations from this baseline were compared with global deviations derived from the PCA model. The results revealed explainable behavioral differences among residents and highlighted three lifestyle archetypes like active bimodal, stable routine, and early resting. By linking the difference scores to contextual activities such as sleep, hygiene, and computer use, the framework provides relevant explanations for daily irregular behaviors.

The rapid rise in internet access and smartphone use has significantly changed how children and adolescents engage in screen-based activities. To date, no systematic review has examined long-term trends in screen time use among children and adolescents that cover periods before and after the onset of the COVID-19 pandemic. This systematic review examined repeated cross-sectional studies to determine whether screen time use among children and adolescents changed over time. This systematic review was registered with PROSPERO (ID: CRD42021243869). The Web of Science, PubMed, Embase, and PsycINFO databases were searched to identify peer-reviewed studies that had been published in English, included data from at least two time points, and focused on children and adolescents between 0 and 19 years of age. The search was conducted without any restrictions on publication year. This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Study quality was assessed using the Quality Assessment Tool for Studies with Diverse Designs. A narrative synthesis was conducted following the Synthesis Without Meta-analysis guidelines. This review identified 60 studies covering the period 1991-2022. The findings indicate that traditional TV watching declined while the use of computers and video games grew. Screen time increased significantly over the years, especially after the COVID-19 pandemic started. The studies reviewed varied in how they defined and measured screen time. The review underscores the importance of continued research and evidence-based policies to guide responsible technology use in the lives of young people. The rapid spread of internet access and smartphones has changed how children and adolescents use digital technologies in their everyday lives. This systematic review looked at 60 studies from around the world to understand how screen time has changed over time from 1991 to 2022, including during the COVID-19 pandemic. Before the pandemic, screen use was increasing gradually, with traditional TV watching decreasing while computer use, video gaming, and mobile phone use grew. The COVID-19 pandemic caused a sharper rise in screen time as children and teenagers relied on screens for school, social interactions, and entertainment during lockdowns. Studies showed that both boys and girls spent more time on screens, though boys tended to spend more time gaming. Younger children generally spent less time on screens than older children, and children from higher-income families often spent slightly less time on screens, although the pandemic increased their screen use too. The review also highlighted that studies measure screen time in different ways, making it hard to compare results. Most studies focused on how long children spent on screens, but few looked at what they were actually doing online. While technology provides benefits like learning, social connection, and creativity, excessive screen time can be linked to problems such as poor sleep, lower physical activity, weight gain, mental health challenges, and exposure to harmful online content. The review concludes that screen time among children and teenagers has generally increased over the past decades, especially after the pandemic started. It recommends that parents, schools, and communities guide young people to use technology in a balanced and safe way, combining digital skills with physical activity, sleep, and offline experiences. Future research should explore not just how much time children spend on screens, but also the type and quality of screen activities.

This exploratory study investigated the impact of computer use on physician performance during clinical simulations. Standardized patient (SP) scenarios used in family practice certification examinations were adapted to include the use of the electronic health record (EHR). The goal was to compare the impact of EHR use during simulated virtual patient encounters on resident physicians' and staff physicians' patient-centeredness (PC) and overall clinical performance, as well as to measure the cognitive load (CL) imposed by EHR use. Sixteen participants each completed 2 video telemedicine simulations with SPs. One simulation case included limited past medical history for the SP in the EHR, while the other did not. Participants were instructed to completely document the encounter using the EHR. Participants' self-perceived CL was measured using the raw National Aeronautics and Space Administration Task Load Index (NASA-TLX). Video recordings were analyzed for participant PC and overall clinical performance. In addition to interacting with the EHR, multiple participants also conducted internet searches. The proportion of time that participants spent interacting with the computer, either using the EHR or searching the internet, was calculated. Inductive qualitative coding of a subset of video recordings (18 of 32 encounters) was performed, with a focus on signs of stress/CL. All videos were assessed for usability problems. Staff physicians (n=6) scored higher on PC compared to resident physicians (n=10) for both cases, though differences were not statistically significant after correction for multiple comparisons (family-wise error rate). Physicians' overall CL, as measured by the raw NASA-TLX, was not significantly correlated with computer use. Exploratory qualitative data analysis found both verbal and nonverbal signs of stress/CL due to computer use while interacting with the SPs. The proportion of time displaying nonverbal signs of stress/CL was calculated for a subset of participants (6 resident physicians and 3 staff physicians). Participant interpretations of instructions to completely document the encounter using the EHR varied widely. It is likely that participants' usual style of documenting, either primarily during or after patient encounters, impacted their use of the EHR while SPs were present. Use of the computer during video telemedicine appointments may negatively impact physician PC and overall clinical performance. Exploratory qualitative coding identified both verbal and nonverbal signs of stress/CL when participants interacted with the computer and the patient simultaneously. Increased clinical experience helped to mitigate the negative impact of computer use. If the use of the EHR is included in physician certification examinations, clear instructions regarding which tasks must be completed in the EHR during interactions with SPs should be provided.

Associations between television/computer use and dementia in socially inactive older adults remain unclear, and optimal limits are unknown. We followed 89,671 dementia-free, socially inactive adults aged &#x2265;55 from UK Biobank for a mean of 12.2 years. Adjusted Cox models assessed associations with incident all-cause dementia and subtypes. Computer use &#x2264;2.4&#xa0;h/day was associated with lower all-cause dementia risk (hazard ratio [HR] 0.88; 95% confidence interval [CI] 0.82-0.94), whereas higher use increased risk (HR 1.19, 95% CI 1.05-1.34); patterns were similar for Alzheimer's and vascular dementia. Television viewing showed no association below 2.06&#xa0;h/day but higher risk thereafter (HR 1.17; 95% CI 1.03-1.32), with a roughly linear increase for vascular dementia. Heavy computer use in apolipoprotein E (APOE) -&#x3b5;4 homozygotes and higher television viewing in adults&#xa0;<&#xa0;65 were more harmful. In socially inactive older adults, moderate computer use may be protective, whereas higher computer use and television viewing are linked to increased dementia risk.

The rapid integration of artificial intelligence (AI) technologies in education highlights the urgency of understanding pre-service teachers' readiness to adopt these tools effectively. Although prior research has separately examined AI self-efficacy, AI anxiety, and generative AI acceptance, few studies have investigated their interrelations within a unified framework. This study linguistically adapted and psychometrically validated the Turkish version of the Brief General AI Self-Efficacy Scale (GSE-6AIS) and explored its associations with AI anxiety, generative AI acceptance, and demographic characteristics. Data were collected from 941 pre-service teachers (52.4% female, 47.6% male; M&#x202f;=&#x202f;21.97 years, SD = 1.45) recruited via convenience sampling from seven Turkish universities. Confirmatory factor analyses supported the scale's unidimensional structure and internal consistency, and multi-group analyses indicated gender invariance. The results showed that higher AI knowledge predicted greater self-efficacy and generative AI acceptance, and lower AI anxiety, whereas gender and computer use showed no significant effects. Mediation analyses revealed that AI self-efficacy partially mediated the relationship between AI anxiety and generative AI acceptance, highlighting its role as a key psychological mechanism. Integrating Bandura's self-efficacy theory with the Technology Acceptance Model (TAM), findings highlight AI self-efficacy as a central mechanism linking anxiety to generative AI acceptance. These findings indicate that the Turkish AI Self-Efficacy Scale is a reliable and valid measure and underscore the importance of fostering self-efficacy to reduce anxiety and enhance acceptance of generative AI in educational contexts. The results have practical implications for teacher education programs aiming to prepare future educators for the increasing presence of AI in learning environments.