Nevin Manimala

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

JCO Clin Cancer Inform. 2025 Dec;9:e2500178. doi: 10.1200/CCI-25-00178. Epub 2025 Dec 10.

ABSTRACT

PURPOSE: Electronic patient portals can promote patient-centered care, but determinants of engagement remain underexplored in oncology. This study examines sociodemographic and clinical factors associated with engagement with four portal features, including invitations to complete patient-reported outcome (PRO) measures before appointments.

METHODS: Secondary analysis of the Northwestern University IMproving the Management of symPtoms during and following Cancer Treatment study, a stepped-wedge cluster randomized trial to promote symptom management using PROs in adult oncology care was performed. For each enrolled participant, we examined portal usage across 1 year.

RESULTS: A total of 3,457 patients were enrolled between April 2020 and April 2023 from 30 Northwestern Medicine ambulatory oncology clinics. Patients were 65% female, 85% White, and 85% non-Hispanic/Latino, with a mean age of 60.8 years. Cancer diagnoses were 30% breast, 12% lymphoma, and all other types accounted for <10% of the sample. Patients accessed laboratory results most frequently (median 23 days in the year), followed by messaging (median 11 days) and physician notes (median 2 days). A total of 62.6% of patients completed at least one invited PRO. Controlling for sociodemographic factors, patient characteristics that were associated with greater engagement across three or more features included more oncology appointments, high health literacy, high anxiety, one or more severe physical symptoms, and high shared decision making with their health care team. Black race, Hispanic/Latino ethnicity, and Medicaid insurance were associated with lower portal engagement. Patients who used any other portal features were more likely to complete PROs. In contrast to other portal features, patients with at least one severe physical symptom were less likely to complete PROs (incidence rate ratio, 0.87 [95% CI, 0.81 to 0.93]; P < .001).

CONCLUSION: Portal use among patients with cancer varies by sociodemographic and clinical characteristics. Findings suggest a need for targeted interventions to promote equitable use among under-represented groups and promote portal-based PRO completion for patients with higher symptom burden.

PMID:41370780 | DOI:10.1200/CCI-25-00178

Clin Neuropsychol. 2025 Dec 10:1-26. doi: 10.1080/13854046.2025.2592660. Online ahead of print.

ABSTRACT

Objective: Ecological momentary assessment (EMA) is a popular method for analyzing intra-individual variability (IIV) of psychological constructs, including cognition. Multilevel modeling (MLM) is a widely used method for analyzing EMA data in intensive longitudinal designs. This systematic review examines how psychologists use and report MLM in EMA studies. It evaluates (1) adherence to the Checklist for Reporting EMA Studies (CREMAS) guidelines, (2) common factors reported in addition to the CREMAS guidelines, and (3) consistency in reporting MLM to analyze EMA data, aiming to improve research design and reporting consistency in the field. Method: Phase 1 searched research databases to explore the commonly used statistical analyses for EMA data. Subsequently, a systematic review was conducted of psychological research articles published between January 2021 and February 2023 which used MLM as the primary method to analyze EMA data. Phase 2 comprised an updated systematic review of articles published from November 2024 to April 2025 to examine whether reporting patterns improved across time. Results: Phase 1 confirmed MLM is the most often statistical procedure used to analyze EMA. 43 articles were reviewed and found (1) generally strong adherence to the CREMAS guidelines, (2) additional components commonly reported, and (3) varied reporting of MLM data preparation and analysis. Phase 2 reviewed 14 articles and found similar results as Phase 1. Conclusions: To further increase transparency and standardize reporting, we recommend several additions to the CREMAS guidelines and a set of Reporting MLM in EMA studies (REMMES) guidelines for future research.

PMID:41370712 | DOI:10.1080/13854046.2025.2592660

Georgian Med News. 2025 Oct;(367):176-180.

ABSTRACT

BACKGROUND: Tracheoesophageal fistula with esophageal atresia (TEF/EA) is a rare congenital disease which has high morbidity and complications. However, there are various factors which can increase the risk of mortality among TEF/EA patients.

AIM: The study’s objective was to assess the features and results of treatment for patients with tracheoesophageal fistula (TEF) and esophageal atresia. Another goal of the study was to evaluate and determine how related anomalies and syndromes affected the course of treatment.

SETTING & DESIGN: a retrospectively designed study was conducted at the Maternity and Children’s Hospital in Qassim, Saudi Arabia.

METHOD & MATERIALS: Patients underwent surgical treatment for TEF/EA were included in the study. Electronic records were used to extract the data. Hence, all the data for all required variables were extracted on excel sheet.

STATISTICAL ANALYSIS USED: a statistical package of social sciences (SPSS) was used. Median, Interquartile range (IQR) and frequency distributions were tabulated as a part of descriptive analysis of the data. For the inferential analysis, chi-square test and odds ratios were computed. All P-values less than .05 were considered statistically significant.

RESULTS: Findings of the study revealed that the presence of associated anomalies (p=.003) and associated syndromes (p=.016) was significantly correlated with non-survival. In addition, associated anomalies were present in all non-survivors (P=.003), and associated syndrome was detected in 3 out of 4 non-survivors (P=.016).

CONCLUSION: The mortality rate was found to be strongly correlated with certain demographic variables, such as birth weight and gender. Furthermore, compared to their counterparts, patients with multiple anomalies and related syndromes had a higher death rate.

PMID:41370701

Georgian Med News. 2025 Oct;(367):169-175.

ABSTRACT

RESEARCH OBJECTIVE: This research aims to examine the association between labor participation and mental health of older adults, particularly depressive symptoms, and to investigate the extent to which this relationship varies across gender and socioeconomic backgrounds. Based on data from the China Longitudinal Aging Social Survey (CLASS), the research examined the mechanisms through which labor participation influenced the mental health of older adults and provided theoretical support and practical guidance for policymakers.

MATERIALS AND METHODS: This research utilized data from the 2023 China Longitudinal Aging Social Survey (CLASS), involving 10,366 older adults aged 60 and above. Employing a cross-sectional design, the research assessed depressive symptoms using the Center for Epidemiologic Studies Depression Scale (CES-D). Labor participation was measured through the questionnaire item, “whether engaged in paid work.” Descriptive statistics, univariable analysis, and multiple linear regression analysis explored the relationship between labor participation and mental health of older adults. Gender-stratified analyses were conducted to examine potential heterogeneity, and further heterogeneity analysis based on job types was performed to examine employment quality.

RESULTS: Labor participation showed a significant association with depressive symptoms among older adults, with those engaged in labor exhibiting lower levels of depressive symptoms than their non-working counterparts. Gender analysis revealed that labor participation exerted a significantly greater association with depressive symptom among women than men. Heterogeneity analysis further revealed that a significant negative association with depressive symptoms was strongest only for work characterized by high autonomy and low physical demands. Additionally, factors such as educational attainment, health status, and marital status significantly influenced depressive symptoms. Labor participation interacted with these factors, jointly influencing the mental health of older adults.

CONCLUSION: A significant association was found between labor participation and reduced depressive symptoms, particularly among women. Furthermore, this relationship varied by job type, showing the strongest association in high-autonomy, low-physical-demand positions. Policy interventions should not only encourage labor participation-particularly among women-but also prioritize job quality by creating positions with greater autonomy and manageable physical demands. Enhancing these job characteristics can strengthen social participation and self-efficacy, thereby maximizing the mental health benefits of working in later life.

PMID:41370700