Tag: statistics

JACC Adv. 2025 Dec 8;5(1):102460. doi: 10.1016/j.jacadv.2025.102460. Online ahead of print.

NO ABSTRACT

PMID:41370863 | DOI:10.1016/j.jacadv.2025.102460

Curr Opin Neurobiol. 2025 Dec 9;96:103144. doi: 10.1016/j.conb.2025.103144. Online ahead of print.

ABSTRACT

Predictive processing frameworks have emphasized the role of forward prediction as a critical ingredient for learning and perceptual inference. We anticipate sensory events that are likely in the future on the basis of past and current sensory events. By comparing these forward predictions against incoming input, we can obtain an accurate estimate of the environment (i.e. perceive) and improve the predictions themselves (i.e. learn). Interestingly however, research in the field of statistical learning has taught us that backward predictive relationships – reflecting the probability of past events given present events – are learnt equally well. This questions the privileged status of forward-looking mechanisms. Here we discuss commonalities and differences between implications for learning and perception. We conclude that while forward and backward predictive relationships both shape learning, we retrieve future, but not past, predicted states during perception.

PMID:41370861 | DOI:10.1016/j.conb.2025.103144

Cancer Discov. 2025 Dec 11. doi: 10.1158/2159-8290.CD-25-1493. Online ahead of print.

ABSTRACT

We estimated trends in age-standardized childhood and adolescent cancer rates between 2001-2022 using national data from the United States Cancer Statistics database. The incidence of cancer in 0-19-year-olds was 18.2 per 100,000 with rates increasing 0.94%/year between 2001-2016 and then decreasing 0.96%/year during 2016-2022. Lymphoma rates increased 0.49%/year during 2001-2022, while trajectories of other cancers varied over time. Leukemia rates increased by 1.03%/year during 2001-2010 and then plateaued. Rates of central nervous system tumors increased by 0.81%/year during 2001-2014 and then declined 2.10%/year during 2014-2022. Rates of other epithelial neoplasms were stable from 2001-2013, increased in 2013-2016, and were stable during 2016-2022. There were an estimated 1,040 additional childhood cancer diagnoses in 2022 compared with what would have been expected based on 2001 rates. Modifications in cancer classifications, screening practices, and diagnostic technology likely contributed to the observed changes, in addition to the potential contributions of putative risk factors.

PMID:41370847 | DOI:10.1158/2159-8290.CD-25-1493

JMIR Mhealth Uhealth. 2025 Dec 10;13:e79347. doi: 10.2196/79347.

ABSTRACT

Regular physical activity offers extensive health benefits, yet current consumer wearables struggle to accurately quantify these effects at an individualized level. Sensor performance often falls short due to susceptibility to interferences, nonstandardized validation, and reliance on indirect estimations. Further, sensors often cannot capture or account for disparities in measurement types, populations, and physiological or anatomical characteristics, nor can they account for how different exercise modalities affect results on a personalized scale. There is a drive for developers to refine the impact of how we measure the benefits of exercise, improving the usefulness of data through advanced optical modeling and spectroscopic applications. This review critically examines the shortcomings of prevailing noninvasive measurements and techniques used in common, commercially available fitness trackers and describes why it is difficult to quantify the effects of exercise as an individualized, quality-based metric. Next, we discuss newer sensing applications that attempt to curtail known limitations, some of which may unveil novel biometric insights through differentiated approaches, bridging gaps not only in technological advancement but also in physiological metrology. In conclusion, we believe that new sensing techniques should explore solutions beyond population-based statistics and aim to provide an individualized understanding of a person’s response to exercise, while also reducing disparities in personalized health monitoring. The results could lead to a more effective understanding of exercise efficacy and its impact on performance management and clinical outcomes.

PMID:41370827 | DOI:10.2196/79347

J Med Internet Res. 2025 Dec 10;27:e76999. doi: 10.2196/76999.

ABSTRACT

BACKGROUND: Health care providers must carefully monitor patients receiving long-term opioid therapy (LTOT) to minimize risks and maximize benefits. Yet, algorithms to support intervention during patient encounters are lacking, with accurate LTOT identification in routine care being the essential first step.

OBJECTIVE: This study aims to develop and validate an LTOT identification algorithm using electronic health record (EHR) data.

METHODS: In this cross-sectional study, we used 2016-2021 OneFlorida+ EHR data linked with Florida Medicaid claims to identify patients aged ≥18 years who received opioid prescriptions. The main outcome was the first LTOT episode in the algorithm development (2016-2018) and validation (2019-2021) periods. A Medicaid claims-based LTOT algorithm served as the reference standard, defined as ≥90 days of continuous opioid use with ≤15-day gaps. Given strong correlations among covariates, an elastic net regression model was applied to identify LTOT episodes in EHR data using patient characteristics, clinically relevant features, and medication use, and to evaluate the model’s classification performance. We randomly split the 2016-2018 cohort into development and internal validation datasets (2:1 ratio), stratified by LTOT incidence. External validation was performed using 2019-2021 data.

RESULTS: Among 64,206 eligible patients identified in 2016-2018 (mean age 35.7, SD 12.3 years; 51,421/64,206, 80.1% female), a total of 8899 (13.9%) had LTOT. Among 50,009 eligible patients identified in 2019-2021 (mean age 37.3, SD 12.5 years; 39,866/50,009, 79.7% female), a total of 6000 (12%) had LTOT. The model selected 29 out of 131 candidate features. Among 2967 individuals with LTOT in the 2016-2018 OneFlorida+ internal validation dataset, a total of 2176 (73.3%) individuals were identified in the top 3 deciles of risk scores. The model achieved a C-statistic of 0.83 (95% CI 0.82-0.84), with 73.4% (95% CI 71.8%-75%) sensitivity, 76.8% (95% CI 76.2%-77.4%) specificity, 33.8% (95% CI 33.1%-34.6%) precision, 76.3% (95% CI 75.8%-76.9%) accuracy, and an F₁-score of 0.46. In the 2019-2021 OneFlorida+ external validation dataset, a total of 75.5% (4527/6000) individuals were correctly captured in the top 3 risk subgroups. The model achieved a C-statistic of 0.83 (95% CI 0.83-0.84), with 78.8% (95% CI 77.8%-79.9%) sensitivity, 73.3% (95% CI 72.9%-73.7%) specificity, 28.7% (95% CI 28.3%-29.1%) precision, 73.9% (73.6%-74.3%) accuracy, and an F₁-score of 0.42.

CONCLUSIONS: The EHR-based LTOT algorithm showed comparable accuracy to the claims-based reference and may support risk stratification and inform decision-making during clinical encounters.

PMID:41370825 | DOI:10.2196/76999

JMIR Res Protoc. 2025 Dec 10;14:e83983. doi: 10.2196/83983.

ABSTRACT

BACKGROUND: Regular participation in some type of physical activity brings improvements in health indicators such as cardiorespiratory fitness, muscle strength, and body composition. However, despite evidence indicating health benefits, 1 in 4 adults is physically inactive, a situation that also occurs in the university population. Walking is a physical activity modality that can be easily incorporated into daily activities; therefore, using a walking-based physical activity intervention could improve some health indicators.

OBJECTIVE: This protocol aims to analyze the impact of a walking-based physical activity intervention on health indicators in university students.

METHODS: An intervention group (n=99) and a control group (n=99) will be randomly selected. All participants will be assessed at the beginning and end of the intervention for indicators of health, cardiorespiratory fitness, muscle strength, and body composition. The intervention group will participate in a 14-week walking program with individualized daily goals, self-monitoring, personalized feedback, and weekly educational material, while the control group will only record their steps without receiving personalized goals or feedback.

RESULTS: The recruitment process will begin in March 2026. Initial assessments are scheduled to take place from March 2, 2026, to March 13, 2026. The intervention will be performed from March 16, 2026, to June 19, 2026 (14 weeks). From June 22, 2026, to July 6, 2026, the final evaluations will be performed. The final results of this study are expected to be published by October 2026.

CONCLUSIONS: This protocol proposes a novel and feasible approach to overcome common barriers to physical activity in university students, with the potential for large-scale application in similar contexts.

TRIAL REGISTRATION: ClinicalTrials.gov NCT06580769; https://clinicaltrials.gov/study/NCT06580769.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/83983.

PMID:41370823 | DOI:10.2196/83983

JMIR Form Res. 2025 Dec 10;9:e74195. doi: 10.2196/74195.

ABSTRACT

BACKGROUND: Google Street View (GSV) images offer a unique and scalable alternative to in-person audits for examining neighborhood built environment characteristics. Additionally, most prior neighborhood studies have relied on cross-sectional designs.

OBJECTIVE: This study aimed to use GSV images and computer vision to examine longitudinal changes in the built environment, demographic shifts, and health outcomes in Washington, DC, from 2014 to 2019.

METHODS: In total, 434,115 GSV images were systematically sampled at 100 m intervals along primary and secondary road segments. Convolutional neural networks, a type of deep learning algorithm, were used to extract built environment features from images. Census tract summaries of the neighborhood built environment were created. Multilevel mixed-effects linear models with random intercepts for years and census tracts were used to assess associations between built environment changes and health outcomes, adjusting for covariates, including median age, percentage male, percentage Hispanic, percentage African American, percentage college educated, percentage owner-occupied housing, and median household income.

RESULTS: Washington, DC, experienced a shift toward higher-density housing, with non-single-family homes rising from 66% to 72% of the housing stock. Single-lane roads increased from 37% to 42%, suggesting a shift toward more sustainable and compact urban forms. Gentrification trends were reflected in a rise in college-educated residents (16%-41%), a US $17,490 increase in the median household income, and a US $159,600 increase in property values. Longitudinal analyses revealed that increased construction activity was associated with lower rates of obesity, diabetes, high cholesterol, and cancer, while growth in non-single-family housing was correlated with reductions in the prevalence of obesity and diabetes. However, neighborhoods with higher proportions of African American residents experienced reduced construction activity.

CONCLUSIONS: Washington, DC, has experienced significant urban transformation, marked by substantial changes in neighborhood built environments and demographic shifts. Urban development is associated with reduced prevalence of chronic conditions. These findings highlight the complex interplay between urban development, demographic changes, and health, underscoring the need for future research to explore the broader impacts of neighborhood built environment changes on community composition and health outcomes. GSV imagery, along with advances in computer vision, can aid in the acceleration of neighborhood studies.

PMID:41370817 | DOI:10.2196/74195

JMIR Public Health Surveill. 2025 Dec 10;11:e77172. doi: 10.2196/77172.

ABSTRACT

BACKGROUND: Many eligible infants and children do not participate in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC); coverage declines throughout the preschool period of eligibility. National and state-level social marketing campaigns promote the value of WIC and increase enrollment and participation. Local contextualization and targeting of materials may increase effectiveness, considering the diversity of families eligible for the program. However, there are few examples of such approaches and their impact.

OBJECTIVE: This study evaluated the impact on child retention of a locally contextualized and targeted social media marketing campaign directed to WIC-eligible families living in the minority-majority population of Miami-Dade County, Florida.

METHODS: The digital marketing campaign geographically targeted low-income families with young children with customized static image and video advertisements on Facebook and Instagram, and a bilingual Google Ads campaign. It was implemented in 2 of 15 clinics operated by the Miami-Dade WIC local agency from May 2020 through April 2021. A before and after evaluation used program administrative data to compare the outcomes for infants and children in 2 innovation clinics (n=6162) with 11 comparison clinics (n=41,074) during a baseline period (2019 calendar year) and the implementation period (n=5636 and n=38,241, respectively). Outcome measures included recertification (re-enrollment during a period), retention (active in the program at the end of a period), and participation (household continuous benefit issuance defined as 11 out of 12 mo). Impact was assessed following cluster-adjusted propensity score weighting and difference-in-difference modeling. Household continuous benefit issuance was estimated in households with only an infant or a child.

RESULTS: Overall, 1,994,170 people were exposed to the campaign advertisements; 16.68% engaged with an advertisement. There were 22,983 unique visits to the local program website, 69.6% of which were acquired directly from the campaign. Four of the 5 top-performing advertisements were locally tailored messages and in Spanish. The change in recertification over time was 5.2% points (95% CI 3.4%-7.1%), greater for those in the innovation group than those in the comparison group. For retention and continuous benefit issuance, the absolute difference in change was 5.5% points (95% CI 3.7%-7.3%), and 6.6% points (95% CI 3.5%-9.7%), respectively. Differences in change over time associated with the innovation were qualitatively stronger for infants than for children; the difference in change for recertification was 7.6% points (95% CI 5.1%-10.1%) for infants and 4.0% points (95% CI 2.2%-5.9%) for children.

CONCLUSIONS: Engaging low-income families with young children through a locally contextualized targeted media marketing campaign can improve retention of children in WIC.

PMID:41370793 | DOI:10.2196/77172

JMIR Form Res. 2025 Dec 10;9:e77319. doi: 10.2196/77319.

ABSTRACT

BACKGROUND: While digital health solutions are becoming increasingly sophisticated, simple forms of everyday digital support may offer underexplored opportunities to promote health among older adults. However, evidence remains scarce on whether such teleassistance-based approaches can effectively enhance health literacy and daily self-care, particularly among populations facing socioeconomic and educational disparities.

OBJECTIVE: This study examined whether a 14-week mobile teleassistance intervention could support daily health promotion and improve health literacy and quality of life among older adults, and whether different levels of user engagement were associated with differences in outcomes.

METHODS: This randomized digital pilot study involved 21 older adults (aged ≥60 years) from Ribeirão Preto, Brazil. All participants were assigned to the intervention arm and subsequently categorized into high-engagement (n=11) and low-engagement (n=10) subgroups according to platform-use metrics. The intervention combined weekly teleconsultations, gamified educational quizzes, and guided health-related activities delivered through a mobile app. Outcomes included health literacy (Health Literacy Questionnaire), quality of life (36-Item Short-Form Health Survey), physical activity, and sedentary behavior, assessed at baseline and postintervention. Analyses appropriate for small samples were applied, including frequentist and Bayesian models.

RESULTS: Participants in the high-engagement subgroup showed greater improvements in health literacy compared with those in the low-engagement subgroup (mean change +9.5 vs +9.1 points; time × group: P<.001; Bayes Factors [BF₁₀]=15). Significant interactions also favored higher engagement for selected quality-of-life domains: vitality (P≤.001), functional capacity (P=.02), and general health (P=.02). A group effect was observed for the mental component (P<.001). Physical activity (F2,38=0.95; P=.39; BF_incl=0.68) and sedentary behavior (F1,19=1.12; P=.32; BF_incl=0.53) did not differ significantly between subgroups. Engagement analytics confirmed higher overall platform use in the high-engagement subgroup (mean 6483.8, SD 807.0 vs mean 3345.3, SD 742.7; t19=6.238; P<.001; d=2.73) and more weekly health-activity minutes (mean 5124.3, SD 757.9 vs mean 3120.7, SD 704.3; t19=6.256; P<.001; d=2.73).

CONCLUSIONS: This 14-week randomized digital pilot trial suggests that everyday digital teleassistance may enhance health literacy and specific quality-of-life domains among older adults when engagement is high. However, such support alone appears insufficient to modify physical activity or sedentary behavior in the short term. Larger and longer trials are needed to assess sustainability, scalability, and strategies to address structural inequalities in digital health adoption.

PMID:41370788 | DOI:10.2196/77319

JMIR Ment Health. 2025 Dec 10;12:e79838. doi: 10.2196/79838.

ABSTRACT

BACKGROUND: Many youth rely on direct-to-consumer generative artificial intelligence (GenAI) chatbots for mental health support, yet the quality of the psychotherapeutic capabilities of these chatbots is understudied.

OBJECTIVE: This study aimed to comprehensively evaluate and compare the quality of widely used GenAI chatbots with psychotherapeutic capabilities using the Conversational Agent for Psychotherapy Evaluation II (CAPE-II) framework.

METHODS: In this cross-sectional study, trained raters used the CAPE-II framework to rate the quality of 5 chatbots from GenAI platforms widely used by youth. Trained raters role-played as youth using personas of youth with mental health challenges to prompt chatbots, facilitating conversations. Chatbot responses were generated from August to October 2024. The primary outcomes were rated scores in 9 sections. The proportion of high-quality ratings (binary rating of 1) across each section was compared between chatbots using Bonferroni-corrected chi-square tests.

RESULTS: While GenAI chatbots were found to be accessible (104/120 high-quality ratings, 86.7%) and avoid harmful statements and misinformation (71/80, 89%), they performed poorly in their therapeutic approach (14/45, 31%) and their ability to monitor and assess risk (31/80, 39%). Privacy policies were difficult to understand, and information on chatbot model training and knowledge was unavailable, resulting in low scores. Bonferroni-corrected chi-square tests showed statistically significant differences in chatbot quality in the background, therapeutic approach, and monitoring and risk evaluation sections. Qualitatively, raters perceived most chatbots as having strong conversational abilities but found them plagued by various issues, including fabricated content and poor handling of crisis situations.

CONCLUSIONS: Direct-to-consumer GenAI chatbots are unsafe for the millions of youth who use them. While they demonstrate strengths in accessibility and conversational capabilities, they pose unacceptable risks through improper crisis handling and a lack of transparency regarding privacy and model training. Immediate reforms, including the use of standardized audits of quality, such as the CAPE-II framework, are needed. These findings provide actionable targets for platforms, regulators, and policymakers to protect youth seeking mental health support.

PMID:41370787 | DOI:10.2196/79838