Nevin Manimala

JCO Oncol Pract. 2025 Nov 13:OP2500306. doi: 10.1200/OP-25-00306. Online ahead of print.

ABSTRACT

PURPOSE: The multicenter Symptom Management Implementation of Patient Reported Outcomes in Oncology Consortium developed electronic Symptom Management (eSyM), an electronic health record-based symptom management program, to reduce acute care utilization. We hypothesized that implementing eSyM would also improve self-reported symptom burden and self-efficacy.

METHODS: eSyM was deployed via a pragmatic stepped-wedge cluster randomized trial for adults who started chemotherapy or had surgery for confirmed or suspected GI, gynecologic, or thoracic malignancies across six cancer centers from 2019 to 2023. In parallel, we administered a survey to two distinct cohorts: patients treated before and after eSyM deployment (ie, pre-live and post-live). A REDCap-based survey collected demographic and clinical characteristics and assessed six Patient-Reported Outcomes Measurement Information System measures: self-efficacy for symptom management, pain interference, anxiety, fatigue, depression, and physical function. Differences in mean T-scores were derived for the post-live versus pre-live cohorts among chemotherapy and surgery recipients. Multivariable regression models controlled for relevant patient and clinical characteristics.

RESULTS: The pre-live cohort included 1,043 respondents (490 chemotherapy and 553 surgery); the post-live cohort included 1,046 respondents (535 chemotherapy and 511 surgery). After controlling for other clinical and demographic factors, the post-live chemotherapy cohort reported statistically significantly lower fatigue and anxiety, but the reductions did not meet the clinically meaningful threshold (adjusted mean T-score difference: -1.3 and -1.8, respectively; P < .05). The post-live surgery cohort reported statistically significantly lower fatigue and anxiety; the differences met the clinically meaningful threshold (-2.0 and -2.2, respectively; P < .01).

CONCLUSION: eSyM deployment was associated with reduced symptom burden, but clinically meaningful differences were only observed in fatigue and anxiety scores among surgical patients. Future studies should investigate the mechanisms by which symptom reporting affects patient outcomes, such as improving patient-clinician communication, enhancing clinician attention to symptom management, or increasing patient self-efficacy.

PMID:41232046 | DOI:10.1200/OP-25-00306

JMIR Form Res. 2025 Nov 13;9:e75651. doi: 10.2196/75651.

ABSTRACT

BACKGROUND: Early detection and awareness are critical in reducing the burden of cancer. However, a significant proportion of university students in Bangladesh remains inadequately informed about cancer risks and preventive measures.

OBJECTIVE: This study aimed to assess knowledge gaps and evaluate the attitudes of Bangladeshi university students toward cancer, its prevention, risk factors, and care for affected individuals.

METHODS: A descriptive, cross-sectional survey was conducted among 530 university students aged 20 to 35 years across Bangladesh. Data were collected using an ethically approved, structured internet-based questionnaire between December 2022 and March 2024. The questionnaire assessed sociodemographics, cancer knowledge, awareness of risk factors, personal or familial cancer experiences, and attitudes toward cancer care and policy. Descriptive statistics and chi-square tests were used to analyze categorical data, with a significance threshold of P<.05.

RESULTS: Most participants were aged 21-25 years (406/530, 76.6%) and female (320/530, 60.4%), with the majority enrolled in undergraduate programs (82.8%, 439/530). While 60.8% (322/530) considered themselves somewhat knowledgeable about cancer, only 11.9% (63/530) were very knowledgeable, and 93.6% (496/530) had never undergone any cancer screening. Despite this, 74.3% (394/530) had personal or familial exposure to cancer, with carcinoma reported by 52.8% (280/530) of those affected. Awareness of established risk factors was inconsistent-smoking (90.9%, 482/530) and radiation (86.6%, 459/530) were widely recognized, but only 38.9% (206/530) acknowledged aging, 35.3% (187/530) obesity, and 29.2% (155/530) infectious agents as risk factors. Reproductive factors were least recognized, with just 10.2% (54/530) identifying having more children as a risk factor. Gender differences were significant in cancer-related attitudes. For example, 51.5% (273/530) of female participants versus 33.4% (177/530) of male participants felt comfortable around patients with cancer (P=.01), and 57.2% (303/530) of female participants versus 35.8% (190/530) of male participants supported increased government funding for cancer care (P=.03). Furthermore, 55.1% (292/530) of females and 35.5% (188/530) of males stressed the need for enhanced cancer awareness programs (P=.05). Only 6.4% (34/530) of all participants reported undergoing any form of cancer screening, highlighting a disconnect between awareness and preventive action.

CONCLUSIONS: This study reveals critical gaps in cancer awareness among university students in Bangladesh, with pronounced disparities in knowledge of nonmodifiable risk factors and significant gender-based differences in attitudes toward cancer care. These findings highlight the urgent need for targeted, gender-sensitive educational programs and policy interventions to promote preventive practices, early detection, and equitable cancer care. Such initiatives must emphasize lesser-known risk factors, reduce stigma, and foster more inclusive, culturally competent health education strategies to mitigate the growing cancer burden in Bangladesh.

PMID:41232041 | DOI:10.2196/75651

JMIR Mhealth Uhealth. 2025 Nov 13;13:e63642. doi: 10.2196/63642.

ABSTRACT

BACKGROUND: The rapidly expanding digital health landscape offers innovative opportunities for improving health care delivery and patient outcomes; however, regulatory and clinical frameworks for evaluating their key features, effectiveness, and outcomes are lacking. Cardiovascular and mental health apps represent 2 prominent categories within this space. While mental health apps have been extensively studied, limited research exists on the quality and effectiveness of cardiovascular care apps. Despite their potential, both categories of apps face criticism for a lack of clinical evidence, insufficient privacy safeguards, and underuse of smartphone-specific features alluding to larger shortcomings in the field.

OBJECTIVE: This study extends the use of the MINDApps framework to compare the quality of cardiovascular and mental health apps framework to compare the quality of cardiovascular and mental health apps with regard to data security, data collection, and evidence-based support to identify strengths, limitations, and broader shortcomings across these domains in the digital health landscape.

METHODS: We conducted a systematic review of the Apple App Store and Google Play Store, querying for cardiovascular care apps. Apps were included if they were updated within the past 90 days, available in English, and did not require a health care provider’s referral. Cardiovascular care apps were matched to mental health apps by platform compatibility and cost. Apps were evaluated using the M-Health Index & Navigation Database (MIND; MINDApps), a comprehensive tool based on the American Psychiatric Association’s app evaluation model. The framework includes 105 objective questions across 6 categories of quality, including privacy, clinical foundation, and engagement. Statistical differences between the 2 groups were assessed using two-proportion Z-tests.

RESULTS: In total, 48 cardiovascular care apps and 48 matched mental health apps were analyzed. The majority of apps in both categories included a privacy policy; yet, the majority in both samples shared user data with third-party companies. Evidence for effectiveness was limited, with only 2 (4%) cardiovascular care apps and 5 (10%) mental health apps meeting this criterion. Cardiovascular care apps were significantly more likely to be used in external devices such as smartphone-based electrocardiograms and blood pressure monitors.

CONCLUSIONS: Both categories lack robust clinical foundations and face substantial privacy challenges. Cardiovascular apps have the potential to revolutionize patient monitoring; yet, their limited evidence base and privacy concerns highlight opportunities for improvement. Findings demonstrate the broader applicability of the MINDApps framework in evaluating apps across medical fields and stress the significant shortcomings in the app marketplace for cardiovascular and mental health. Future work should prioritize evidence-based app development, privacy safeguards, and the integration of innovative smartphone functionalities to ensure that health apps are safe and effective for patient use.

PMID:41232040 | DOI:10.2196/63642

JMIR Mhealth Uhealth. 2025 Nov 13;13:e69028. doi: 10.2196/69028.

ABSTRACT

BACKGROUND: Parkinson disease (PD) is a progressive neurodegenerative disorder characterized by motor and nonmotor symptoms that worsen over time, significantly impacting quality of life. While clinical evaluations such as the Unified Parkinson’s Disease Rating Scale (UPDRS) are standard for assessing disease severity, they offer somewhat limited temporal resolution and are susceptible to observer variability. Smartphone apps present a viable method for capturing detailed fluctuations in motor and vocal functions in real-world settings.

OBJECTIVE: This study aimed to use a smartphone-based app to quantitatively evaluate the interaction effect between time and disease severity on motor and vocal symptoms in individuals with PD.

METHODS: This was an exploratory, cross-sectional pilot study. Disease severity in persons with PD was assessed using the modified Hoehn & Yahr Scale, Voice Handicap Index, and UPDRS. We used a custom smartphone app to administer finger-tapping tasks, sustained phonation (/a/ and /i/), and rapid syllable repetition (/dadada/ and /pa-ta-ka/). The total tap counts, tap-to-tap variability, and vocal parameters (loudness, jitter, shimmer, repeat counts, and their variability) were analyzed. Each task was divided into 5 equal time frames to analyze performance changes over a short duration. Time-severity interactions were examined using linear mixed models.

RESULTS: In total, 20 persons with PD and 20 healthy adults were included in this study. Persons with PD showed worse motor and vocal performance compared to healthy adults, with higher dysrhythmia; worse jitter, shimmer, and jitter and shimmer variability; and fewer repeat counts. During finger-tapping tasks, individuals with PD showed an earlier onset of dysrhythmia than their healthy counterparts. While a higher UPDRS part III score was associated with greater finger-tapping variability, there was no significant time-severity interaction for this motor task. However, linear mixed model analysis revealed significant time-severity interaction effects for vocal tasks, including /a/ loudness (P=.001), /a/ jitter (P=.01), /a/ shimmer (P=.001), /i/ loudness (P=.001), /i/ jitter (P<.001), /i/ shimmer (P<.001), and /pa-ta-ka/-variability (P=.04). This indicates that individuals with higher UPDRS part III scores experienced a more rapid decline in vocal control during the assessment period. All measured smartphone-based characteristics showed a significant correlation with UPDRS part III scores, with finger-tapping variability having the strongest correlation.

CONCLUSIONS: This study demonstrates that a smartphone-based assessment, conducted over just a few minutes, can detect subtle temporal changes in fine motor and vocal control. The app successfully captured the earlier onset of dysrhythmia in individuals with PD and, importantly, identified significant time-severity interaction effects in vocal performance. This suggests that such digital tools can provide sensitive, dynamic insights into symptom progression, potentially enabling more precise monitoring and timely clinical interventions for individuals with PD.

PMID:41232035 | DOI:10.2196/69028

JMIR Med Inform. 2025 Nov 13;13:e76659. doi: 10.2196/76659.

ABSTRACT

BACKGROUND: Cardiovascular disease (CVD) remains a leading cause of preventable morbidity and mortality, highlighting the need for early risk stratification in primary prevention. Traditional Cox models assume proportional hazards and linear effects, limiting flexibility. While machine learning offers greater expressiveness, many models rely solely on structured data and overlook time-to-event (TTE) information. Integrating structured and textual representations may enhance prediction and support equitable assessment across clinical subgroups.

OBJECTIVE: This study aims to develop a hybrid multitask deep learning model (MT-BERT [multitask Bidirectional Encoder Representations from Transformers]) integrating structured and textual features from electronic health records (EHRs) to predict 10-year CVD risk, enhancing individualized stratification and supporting equitable assessment across diverse demographic groups.

METHODS: We used data from Clinical Practice Research Datalink (CPRD) Aurum comprising 469,496 patients aged 40-85 years to develop MT-BERT for 10-year CVD risk prediction. Structured EHR variables and their corresponding textual representations were jointly encoded using a multilayer perceptron and a distilled version of the BERT model (DistilBERT), respectively. A fusion layer and stacked multihead attention modules enabled cross-modal interaction modeling. The model generated both binary classification outputs and TTE risk scores, optimized using a custom FocalCoxLoss function with uncertainty-based weighting. Prediction targets encompassed composite and individual CVD outcomes. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), concordance index, and Brier score, with subgroup analyses by ethnicity and deprivation, and heterogeneity assessed using Higgins I² and Cochran Q statistics. Generalizability was assessed via external validation in a held-out London cohort.

RESULTS: The MT-BERT model yielded AUROC values of 0.744 (95% CI 0.738-0.749) in males and 0.782 (95% CI 0.768-0.796) in females on the test set (n=711,052), and 0.736 (95% CI 0.729-0.741) and 0.775 (95% CI 0.768-0.780), respectively in “spatial external” validation (n=144,370). Brier scores were 0.130 in males and 0.091 in females. Individuals classified as high-risk (≥40% risk in males and ≥34% in females) demonstrated significantly reduced 10-year event-free survival relative to lower-risk individuals (log-rank P<.001). Model performance was consistently higher in females across all metrics. Subgroup analyses revealed substantial heterogeneity across ethnicity and deprivation (I²>70%), especially among males, with lower AUROC in South Asian and Black ethnic groups. These findings reflect variation in model performance across demographic groups while supporting its applicability to large-scale CVD risk stratification.

CONCLUSIONS: The proposed hybrid MT-BERT model predicts 10-year CVD risk for primary prevention by integrating structured variables and unstructured clinical text from EHRs. Its multitask design facilitates both individualized risk stratification and TTE estimation. While performance was modestly reduced in deprived and minority ethnic subgroups, these findings provide preliminary support for advancing equity-aware, data-driven prevention strategies in increasingly diverse health care settings.

PMID:41232034 | DOI:10.2196/76659

JMIR Infodemiology. 2025 Nov 13;5:e75973. doi: 10.2196/75973.

ABSTRACT

BACKGROUND: Social media platforms are increasingly used for both sharing and seeking health-related information online. TikTok has become one of the most widely used social networking platforms. One health-related topic trending on TikTok recently is attention-deficit/hyperactivity disorder (ADHD). However, the accuracy of health-related information on TikTok remains a significant concern. Misleading information about ADHD on TikTok can increase stigmatization and lead to false “self-diagnosis,” pathologizing of normal behavior, and overuse of care.

OBJECTIVE: This study aims to investigate the quality and usefulness of popular TikTok videos about ADHD and to explore how this content is perceived by the viewers based on an in-depth analysis of the video comments.

METHODS: We scraped data from the 125 most liked ADHD-related TikTok videos uploaded between July 2021 and November 2023 using a commercial scraping software. We categorized videos based on the usefulness of their content as “misleading,” “personal experience,” or “useful” and used the Patient Education Materials Assessment Tool for Audiovisual Materials to evaluate the video quality regarding understandability and actionability. By purposive sampling, we selected 6 videos and analyzed the content of 100 randomly selected user comments per video to understand the extent of self-identification with ADHD behavior among the viewers. All qualitative analyses were carried out independently by at least 2 authors; the disagreement was resolved by discussion. Using SPSS (version 27; IBM Corp), we calculated the interrater reliability between the raters and the descriptive statistics for video and creator characteristics. We used one-way ANOVA to compare the usefulness of the videos.

RESULTS: We assessed 50.4% (63/125) of the videos as misleading, 30.4% (38/125) as personal experience, and 19.2% (24/125) as useful. The Patient Education Materials Assessment Tool for Audiovisual Materials scores for all videos for understandability and actionability are 79.5% and 5.1%, respectively. With a score of 92.3%, useful videos scored significantly higher for understandability than misleading and personal experience videos (P<.001). For actionability, there was no statistically significant difference depending on the videos’ usefulness (P=.415). Viewers resonated with the ADHD-related behaviors depicted in the videos in 220 out of 600 (36.7%) of the comments and with ADHD in 32 out of 600 (5.3%) of the comments. Self-attribution of behavioral patterns varied significantly, depending on the usefulness of the videos, with personal experience videos showing the most comments on self-attribution of behavioral patterns (102/600, 17% of comments; P<.001). For self-attribution of ADHD, we found no significant difference depending on the usefulness of the videos (P=.359).

CONCLUSIONS: The high number of misleading videos on ADHD on TikTok and the high percentage of users who self-identify with the symptoms and behaviors presented in these videos can potentially increase misdiagnosis. This highlights the need to critically evaluate health information on social media and for health care professionals to address misconceptions arising from these platforms.

PMID:41232032 | DOI:10.2196/75973

JMIR Public Health Surveill. 2025 Nov 13;11:e65660. doi: 10.2196/65660.

ABSTRACT

BACKGROUND: Racism perpetuates the unequal distribution of power, resources, and privilege within and between societies to the detriment of marginalized groups. Racialization involves categorizing people based on traits to which socially constructed meaning and value have been ascribed. In public health, this process can manifest when tracking racial health disparities in children, which requires aggregating parent-reported race and ethnicity data into federally recognized categories. The demographic surveys used to characterize children’s identity in the United States mirror those administered in adults and typically follow federal race and ethnicity data standards, which include ambiguous response options (eg, other race), “select all that apply” directives, and open-ended fields followed by a request specification, with limited guidance for coding and interpretation. These methodological challenges could contribute to nonrandom data missingness and misclassification bias and must be resolved to better harmonize historic data, especially given recent revisions to the country’s federal race and ethnicity data standards.

OBJECTIVE: We aimed to explore the prevalence of systematic bias within past, current, and recently revised federal race and ethnicity data standards in the United States and develop a standardized method for improving the reporting of child race and ethnicity in public health research, policy, and practice.

METHODS: We developed a replicable decision-making process to uncover racial heterogeneity obscured by key components of US federal race and ethnicity data standards (open-ended and ambiguous response fields). We applied it to a pooled sample of 8 community-based child health studies with 8087 participants and examined changes in the dataset’s racial and ethnic diversity.

RESULTS: Overall, 93.11% (7530/8087) of parents provided child race and ethnicity data, with 3.73% (281/7530) identified as other race and 9.72% (732/7530) identified as multiracial. In total, 101 distinct open-ended written responses (eg, “Haitian”) were provided. The replicable decision-making process resulted in 4.02% (303/7530) of children being reallocated from their parent-reported race or ethnicity category, of whom 38.6% (117/303) were moved into the Black category based on written responses. Within the multiracial group, we identified 22 unique combinations, including White-Hispanic (269/732, 36.7%) and White-Black (169/732, 23.08%).

CONCLUSIONS: These findings demonstrate how the current paradigm of assessing race and ethnicity in the United States may contribute to the erasure and further marginalization of individuals disproportionately enduring the effects of racism. While updated federal race and ethnicity data standards may soon take effect, persistent gaps in demographic and health surveillance will remain. Our data reallocation decision-making process offers a novel and practical framework for harmonizing race and ethnicity data across time, populations, and datasets, emphasizing the relevance and longevity of preexisting datasets and tools. Efforts to build equitable public health surveillance and data systems should expand the survey response options, avoid aggregating diverse populations, and develop new statistical techniques for data analysis.

PMID:41232027 | DOI:10.2196/65660

Clin Exp Optom. 2025 Nov 13:1-11. doi: 10.1080/08164622.2025.2578336. Online ahead of print.

ABSTRACT

CLINICAL RELEVANCE: Oxidative stress and elevated reactive oxygen species (ROS) are implicated in corneal pathologies such as keratoconus. Understanding how scleral lens (SL) wear influence these mechanisms is important for optimizing patient management.

BACKGROUND: To assess the alteration in ROS level of keratoconic eyes wearing SL.

METHODS: Ocular surface tears (T₀) were collected at baseline and 6 hours of lens wear (T₆) on Day 1 and 1 month from 26 keratoconic eyes. SL fluid reservoir (SL_FR) was collected after 6 hours of lens wear on Day 1 (SL_FR Day 1) and at 1 month (SL_FR 1 month). ROS levels were measured using fluorimetry from Schirmer’s strip tear samples and the SL_FR samples. Visual acuity, Schirmer’s values, and Ocular Surface Disease index questionnaire was also assessed.

RESULTS: The ROS levels of T₀ 1 month showed significant reduction after 6 hours of SL wear (p < 0.001). Similarly, the ROS level of SL_FR decreased after 1 month of SL wear compared to Day 1 (p = 0.007). ROS level in SL_FR at Day 1 and 1 month showed significant reduction when compared to T₀ 1 month value (p = 0.002, p < 0.001 respectively). A statistically significant increase in T₀ value was noted at 1 month time point when compared to day 1 values (p = 0.005). The baseline ROS levels were higher in collagen cross-linked eyes (p = 0.01), while non-collagen cross-linked eyes showed a reduction in ROS levels of SL_FR (p = 0.01) after 1 month of SL wear. Visual acuity and ocular surface disease index score showed significant improvement after 6 hours and 1 month compared to baseline (p < 0.001 and p < 0.001).

CONCLUSION: ROS level reduced in the SL_FR after 1 month and in tears after 6 hours with SL in KC patients. This indicates the effectiveness of SL in reducing ROS level of SL_FR which might have association with keratoconus pathogenesis, warrants further analysis.

PMID:41232020 | DOI:10.1080/08164622.2025.2578336

J Gen Virol. 2025 Nov;106(11). doi: 10.1099/jgv.0.002174.

ABSTRACT

The influenza A H1N1pdm09 virus continues to pose a significant zoonotic threat, with implications for both animal and human health. Italy, which hosts one of the largest swine populations in Europe, is strategically positioned to monitor the evolution of influenza viruses in livestock. This study addresses the genetic diversity and transmission dynamics of H1N1pdm09 in Italian swine, using whole-genome sequencing and dynamic modelling of samples collected from farms across the country. Our findings indicate multiple independent introductions of H1N1pdm09 into Italy. While most were self-limiting, six distinct transmission clusters suggest localized and sustained spread across various regions. Although many introductions were contained, certain lineages demonstrated the ability to circulate within specific areas. Selective pressure analyses showed strong purifying selection across most viral genes in both swine and human hosts, with non-synonymous to synonymous substitution rate (dN/dS) ratios well below 1. The haemagglutinin gene exhibited a higher dN/dS ratio in swine (~0.28) than in humans (~0.22), indicating slightly relaxed selection in swine. Neuraminidase and non-structural proteins were similarly constrained in both hosts. This study underscores the importance of ongoing genomic surveillance to detect viral circulation and mitigate zoonotic risks. Italy’s contribution supports global influenza monitoring and reinforces the need for a One Health approach that integrates human, animal and environmental health. These insights are crucial for informing public health strategies and improving preparedness for future outbreaks.

PMID:41231533 | DOI:10.1099/jgv.0.002174

Eur J Pain. 2025 Nov;29(10):e70170. doi: 10.1002/ejp.70170.

ABSTRACT

BACKGROUND: Individuals with low back pain (LBP) have high healthcare use (HCU). It is currently unclear whether self-management supportive interventions can decrease HCU among patients with LBP. The aim of this study was to investigate changes in visits to primary care and redeemed prescriptions of analgesics after enrolment in a self-management supportive programme compared to usual care.

METHODS: This quasi-experimental study included adults with LBP who enrolled in the Danish GLA:D Back programme between 2018 and 2022. GLA:D Back is a structured 10-week programme of group-based patient education and supervised exercises aiming to enhance self-management skills. HCU was obtained from national registries as the total quarterly visits to primary care (general practitioner, physiotherapists or chiropractor) or quarterly total redeemed defined daily doses (DDD) of analgesics (paracetamol, non-steroidal anti-inflammatory drugs or opioids).

RESULTS: We included 4205 individuals. From 2 to 14 quarters post-enrolment, the additional quarterly reduction in HCU after the programme compared to the control group was -1.1 (95% CI -1.5 to -0.8) visits to primary care and -5.3 (95% CI -9.2 to -2.2) DDDs of redeemed analgesics. Sensitivity analyses questioned the statistical significance of the reduction in analgesic use, but results for people with LBP duration > 1 year were robust for both outcomes. The largest reductions were observed in those with high HCU at baseline.

CONCLUSION: Participation in a structured self-management programme led to a sustained reduction in primary care visits and analgesic use over a 3-year period, although regression to the mean may partly explain these reductions.

SIGNIFICANCE STATEMENT: This quasi-experimental study demonstrated that a structured self-management supportive programme for low back pain reduced future healthcare use, especially among individuals with long-lasting pain or high initial healthcare use. These findings suggest a potential to alter healthcare use through structured interventions to support self-management.

PMID:41231521 | DOI:10.1002/ejp.70170