JAMA Network Open最新文献

Importance: As generative artificial intelligence (GenAI) tools become increasingly integrated into the daily lives of youth, it is critical to study their usage patterns and potential implications for mental health. While there is evidence of a rapid pace of adoption among adults, rates of GenAI use among youth remains largely undocumented.

Objective: To characterize GenAI application (app) usage among US youth, including adoption rates and time spent.

Design, setting, and participants: This cross-sectional study documented digital behavior of US youth extracted from a parental monitoring app. Participants were ages 4 to 17 years and were in families using a commercially available Aura app in the US. No identifying information was collected about the child except year of birth. Data were collected using passive sensing methods from naturalistic smart device use between September 2024 and April 2025. Data were analyzed in May and June 2025.

Main outcome and measures: Adoption rates (ie, number of youth ever accessing GenAI apps on their device) and time spent using GenAI (ie, average minutes accessing GenAI apps), measured by age and time period.

Results: In a cohort of 6488 participants, nearly 2072 youths (31.9%) used GenAI apps on their device. GenAI use was highest among teens (age 13 to 14 years, 899 of 2139 [42.0%]; age 15 to 17 years, 628 of 1246 [50.4%]), although adoption among preteens (age 10 to 12 years, 484 of 2366 [20.5%]) and school-aged children (age 8 to 9 years, 49 of 522 [9.4%]) was not trivial. GenAI usage was higher after school than at nighttime or during school. Overall, users spent a mean (SD) 2.37 (10.55) and a median (IQR) 0.18 (0.04-0.84) minutes a day using GenAI, yet large variances and skewed distributions suggest that a small subset of youth use GenAI extensively, with the heaviest users accessing GenAI for over 40 minutes a day.

Conclusions and relevance: In this cross-sectional study, Gen AI app use varied widely among participants, with up to half of adolescents having some use and a small subset engaging in heavy use. Future research must address individual differences in GenAI use to determine impacts on development.

Importance: As telehealth (ie, telephone and video) becomes a larger component of primary care, understanding its impact on care quality is critical.

Objective: To evaluate whether the proportion of primary care received via telehealth is associated with differences in quality-of-care outcomes among veterans who frequently use primary care.

Design, setting, and participants: This is a retrospective cohort study of veterans empaneled to Veterans Health Administration (VHA) primary care in fiscal years 2022 and 2023 (October 1, 2021, to September 30, 2023) with 3 or more primary care visits. Telehealth proportion categories were none (0.0% primary care visits telehealth), low (>0.0% to <28.6%), intermediate (28.6% to <50.0%), or high (≥50.0%).

Exposure: Proportion of primary care delivered via telehealth.

Main outcomes and measures: The primary outcomes were influenza vaccination, hypertension control, statin therapy and adherence, and screenings and/or counseling for depression, tobacco, and alcohol use. Multivariable logistic regression was used to estimate adjusted average marginal effects (AMEs), controlling for sociodemographic, geographic, and clinical characteristics.

Results: This study included 744 599 veterans (mean [SD] age, 65 [15] years; 638 289 male [86%]). Compared with veterans receiving in-person care only, those who received a low proportion of care via telehealth had similar quality of outcomes for all cardiovascular and behavioral health measures. Influenza vaccination rates were modestly lower in the low-telehealth group vs the in-person only group (age ≥66 years, AME, -1.93% [95% CI, -2.58% to -1.29%]; age 19-65 years, AME, -1.57% [95% CI, -2.28% to -0.86%]). High telehealth users (≥50% telehealth) had the lowest adjusted likelihoods for most quality outcomes, including influenza vaccination (age ≥66 years, AME, -8.96% [95% CI, -9.84% to -8.07%]; age 19-65 years, AME, -9.72% [95% CI, -10.84% to -8.60%]) statin adherence (AME, -2.03% [95% CI -2.93% to -1.14%]) and depression screening (AME, -2.14% [95% CI, -3.20% to -1.08%]).

Conclusions and relevance: In this cohort study of veterans with 3 or more primary care visits, primary care quality was similar for individuals who received all in-person care and those receiving low or intermediate proportions of telehealth. However, high telehealth use was associated with lower quality for several services, especially those requiring in-person interaction. Findings demonstrate the viability of hybrid telehealth and in-person models. Additional resources might be needed to ensure high-quality primary care for high proportion telehealth users.

Importance: Early parental recognition of severe illness in children and adolescents is crucial for timely management and improved outcomes in pediatric emergency care.

Objective: To assess how accurately parents can identify severe illness in their children using a questionnaire completed shortly after arrival at the emergency department (ED).

Design, setting, and participants: This diagnostic study was conducted in a tertiary pediatric ED in northern Finland. Data were collected in 2019 to 2021, and this analysis was conducted in May 2024 to May 2025. Children and adolescents whose parents completed the questionnaire before physician assessment were included.

Exposures: A structured, 36-item parental questionnaire assessing symptoms and the child or adolescent's overall condition.

Main outcomes and measures: Severe illness was defined as 1 or more of the following: admission to the pediatric intensive care unit, hospital treatment of more than 24 hours, need for intravenous or nasogastric fluids, need for intravenous antibiotics for more than 24 hours, oxygen saturation less than 93% or the need for inhaled medications, anaphylactic shock, intoxication requiring hospital admission, or surgical intervention. Sensitivity and specificity were calculated for each question. To identify parental triage questions with the strongest diagnostic value, a machine learning analysis was conducted.

Results: Among 2375 included children and adolescents (mean [SD] age, 5.4 [4.6] years; 1140 female [48.0%]), 567 individuals (23.9%) met criteria for severe illness. Moderate to high parental worry showed the highest sensitivity (91.0% [95% CI, 88.3%-93.2%]) but the lowest specificity (17.5% [95% CI, 15.8%-19.4%]). Other specific pediatric questions demonstrated modest diagnostic accuracy with limited additional value. The machine learning model (area under the receiver operating characteristic curve, 0.71; 95% CI, 0.65-0.77) identified parental worry (feature importance score, 0.047), parent assessments of child or adolescent's general condition (feature importance score, 0.046), and need for treatment (feature importance score, 0.141) as the strongest predictors of hospital admission.

Conclusions and relevance: In this study, parental worry identified most cases of severe illness but had low specificity. These findings suggest that while parental concern may serve as an initial screening indicator, it should be complemented by clinical evaluation and objective measures to avoid unnecessary escalation of care.

Importance: Preterm infants are at high risk of developing brain injury, and near-infrared spectroscopy (NIRS) offers the ability to measure cerebral oxygenation. The impact of using a standardized treatment guideline combined with a single NIRS device manufacturer (Nonin Medical Inc) and neonatal sensor on cerebral oxygenation has not been previously examined.

Objective: To investigate whether cerebral oximetry with a dedicated treatment guideline improves cerebral oxygenation stability.

Design, setting, and participants: This was a single-blinded, 2-arm randomized clinical trial conducted from October 2021 to July 2024 at 5 tertiary neonatal intensive care units across Australia, New Zealand, and the US. Infants born at less than 29 weeks' gestation and aged younger than 6 hours underwent 1:1 random allocation stratified by gestational age (<26 weeks and ≥26 weeks) and study site.

Intervention: The intervention group received cerebral oximetry and dedicated guideline-based treatment when cerebral oxygenation was outside the range of 65% to 90%. The control group had blinded cerebral oximetry and treatment guided by standard clinical monitoring.

Main outcomes and measures: The burden of cerebral hypoxia and hyperoxia during the first 5 days after birth expressed as percentage hours was the primary outcome. Key secondary outcomes were mortality, morbidities before discharge, and NIRS-related skin injury.

Results: Of 149 screened infants (53 randomized to the intervention and 51 randomized to standard care), 100 infants were included in the final analysis (median [IQR] gestational age, 27 [25-28] weeks; 48 male [48.0%]). The median (IQR) birth weight was 883 (709-1079) g. The intervention group (50 infants) had a significantly lower median (IQR) burden of hypoxia and hyperoxia of 5.7% hours (2.8% hours to 15.0% hours) compared with 39.6% hours (6.5% hours to 82.3% hours) in the standard care group (50 infants), with an adjusted reduction of 42.8% hours (95% CI, 35.6% hours to 53.3% hours; P < .001). Mortality, morbidities before discharge, and safety outcomes were comparable between groups.

Conclusions and relevance: In this study, treatment guided by cerebral oximetry with a single device manufacturer and a neonatal sensor significantly improved the stability of cerebral oxygenation in extremely preterm infants. Larger multicenter trials are warranted to determine if this finding leads to improved survival without brain injury.

Trial registration: Australian New Zealand Clinical Trials Registry registration number ACTRN12621000778886.