Tag: nevin manimala

JMIR Infodemiology. 2025 Apr 2;5:e68483. doi: 10.2196/68483.

ABSTRACT

BACKGROUND: During the COVID-19 pandemic, health experts used social media platforms to share information and advocate for policies. Many of them faced online abuse, which some reported took a toll on their mental health and well-being. Variation in the impacts of online abuse on mental health, well-being, and professional efficacy suggest that health communicators may differ in their coping strategies and ultimately their resilience to such abuse.

OBJECTIVE: We aimed to explore the impacts of online abuse on health communicators’ mental health and well-being as well as their emotion- and problem-focused coping strategies.

METHODS: We recruited health communicators (public health officials, medical practitioners, and university-based researchers) in Canada who engaged in professional online communication during the COVID-19 pandemic. In phase 1, semistructured interviews were conducted with 35 health communicators. In phase 2, online questionnaires were completed by 34 individuals before participating in workshops. Purposive recruitment resulted in significant inclusion of those who self-identified as racialized or women. Interview and workshop data were subjected to inductive and deductive coding techniques to generate themes. Descriptive statistics were calculated for selected questionnaire questions.

RESULTS: In total, 94% (33/35) of interviewees and 82% (28/34) of questionnaire respondents reported experiencing online abuse during the study period (2020-2022). Most health communicators mentioned facing an emotional and psychological toll, including symptoms of depression and anxiety. Racialized and women health communicators faced abuse that emphasized their ethnicity, gender identity, and physical appearance. Health communicators’ most common emotion-focused coping strategies were withdrawing from social media platforms, avoiding social media platforms altogether, and accepting online abuse as unavoidable. Common problem-focused coping strategies included blocking or unfriending hostile accounts, changing online behavior, formal help-seeking, and seeking peer support. Due to the impacts of online abuse on participants’ mental health and well-being, 41% (14/34) of the questionnaire respondents seriously contemplated quitting health communication, while 53% (18/34) reduced or suspended their online presence. Our findings suggest that health communicators who used problem-focused coping strategies were more likely to remain active online, demonstrating significant professional resilience.

CONCLUSIONS: Although health communicators in our study implemented various emotion- and problem-focused coping strategies, they still faced challenges in dealing with the impacts of online abuse. Our findings reveal the limitations of individual coping strategies, suggesting the need for effective formal organizational policies to support those who receive online abuse and to sanction those who perpetrate it. Organizational policies could improve long-term outcomes for health communicators’ mental health and well-being by mitigating online abuse and supporting its targets. Such policies would bolster professional resilience, ensuring that important health information can still reach the public and is not silenced by online abuse. More research is needed to determine whether gender, race, or other factors shape coping strategies and their effectiveness.

PMID:40173443 | DOI:10.2196/68483

J Med Internet Res. 2025 Apr 2;27:e59591. doi: 10.2196/59591.

ABSTRACT

BACKGROUND: The last decade has witnessed major advances in the development of artificial intelligence (AI) technologies for use in health care. One of the most promising areas of research that has potential clinical utility is the use of AI in pathology to aid cancer diagnosis and management. While the value of using AI to improve the efficiency and accuracy of diagnosis cannot be underestimated, there are challenges in the development and implementation of such technologies. Notably, questions remain about public support for the use of AI to assist in pathological diagnosis and for the use of health care data, including data obtained from tissue samples, to train algorithms.

OBJECTIVE: This study aimed to investigate public awareness of and attitudes toward AI in pathology research and practice.

METHODS: A nationally representative, cross-sectional, web-based mixed methods survey (N=1518) was conducted to assess the UK public’s awareness of and views on the use of AI in pathology research and practice. Respondents were recruited via Prolific, an online research platform. To be eligible for the study, participants had to be aged >18 years, be UK residents, and have the capacity to express their own opinion. Respondents answered 30 closed-ended questions and 2 open-ended questions. Sociodemographic information and previous experience with cancer were collected. Descriptive and inferential statistics were used to analyze quantitative data; qualitative data were analyzed thematically.

RESULTS: Awareness was low, with only 23.19% (352/1518) of the respondents somewhat or moderately aware of AI being developed for use in pathology. Most did not support a diagnosis of cancer (908/1518, 59.82%) or a diagnosis based on biomarkers (694/1518, 45.72%) being made using AI only. However, most (1478/1518, 97.36%) supported diagnoses made by pathologists with AI assistance. The adjusted odds ratio (aOR) for supporting AI in cancer diagnosis and management was higher for men (aOR 1.34, 95% CI 1.02-1.75). Greater awareness (aOR 1.25, 95% CI 1.10-1.42), greater trust in data security and privacy protocols (aOR 1.04, 95% CI 1.01-1.07), and more positive beliefs (aOR 1.27, 95% CI 1.20-1.36) also increased support, whereas identifying more risks reduced the likelihood of support (aOR 0.80, 95% CI 0.73-0.89). In total, 3 main themes emerged from the qualitative data: bringing the public along, the human in the loop, and more hard evidence needed, indicating conditional support for AI in pathology with human decision-making oversight, robust measures for data handling and protection, and evidence for AI benefit and effectiveness.

CONCLUSIONS: Awareness of AI’s potential use in pathology was low, but attitudes were positive, with high but conditional support. Challenges remain, particularly among women, regarding AI use in cancer diagnosis and management. Apprehension persists about the access to and use of health care data by private organizations.

PMID:40173441 | DOI:10.2196/59591

J Med Internet Res. 2025 Apr 2;27:e62822. doi: 10.2196/62822.

ABSTRACT

BACKGROUND: As the percentage of the older population increases, it is accompanied by an increase in the prevalence of Parkinson disease (PD). People with PD experience a range of nonmotor symptoms, including pain, constipation, dysphagia, sleep disturbances, and fatigue. Improving self-care is necessary for people with PD because it is a chronic disease that requires lifelong management. In our previous study, we developed a mobile app (Yon PD app) to monitor nonmotor symptoms of PD. In this study, we investigated the long-term effects of the app in a larger group of people.

OBJECTIVE: This study aimed to examine the effectiveness of a mobile app on the management of self-care in people with PD.

METHODS: This was a randomized controlled trial. People with PD aged ≥50 years and able to use a smartphone were recruited from the neurology outpatient clinic of a tertiary hospital in South Korea. In total, 102 participants were enrolled in this study. The intervention group was requested to record 5 nonmotor symptoms (pain, constipation, dysphagia, sleep disturbances, and fatigue) for 12 weeks using the mobile app. The control group was requested to record these 5 nonmotor symptoms on a paper questionnaire. General characteristics including age, sex, level of education, disease severity, and comorbidities were examined at baseline. The degree of self-care was examined using the Self-Care of Chronic Illness Inventory at baseline, 6 weeks, and 12 weeks. At 12 weeks, satisfaction with the app was also examined. General characteristics and satisfaction with the app were analyzed using descriptive statistics. The effect of the app on self-care was analyzed using the repeated-measures ANOVA with an α level of .05.

RESULTS: In total, 93 participants were included in the analysis. There were 41 and 52 participants in the intervention and control groups, respectively. The general characteristics of the 2 groups were comparable. Monitoring nonmotor symptoms with the app effectively increased self-care maintenance (F₂₁₈₂=4.087; P=.02) and prevented a decrease in self-care monitoring (F₂₁₈₂=3.155; P=.045). However, using the app was ineffective in improving self-care management (F₂₁₈₂=1.348; P=.26). Self-care management gradually decreased over the 12-week period in both groups. The intervention (n=41) adherence rate reached 60.84% at 6 weeks but decreased to 41.87% by 12 weeks.

CONCLUSIONS: Participants were able to improve the degree of self-care by monitoring their nonmotor symptoms using the app. However, additional strategies that increase motivation and enjoyment are required to improve adherence.

TRIAL REGISTRATION: Clinical Research Information Service KCT0006433; https://tinyurl.com/3vmf435m.

PMID:40173440 | DOI:10.2196/62822

Psychol Health Med. 2025 Apr 2:1-15. doi: 10.1080/13548506.2025.2487227. Online ahead of print.

ABSTRACT

Pregnancy is associated with an increased psychological burden on women. This burden may be worsened by uncertainty occasioned by pandemics such as the recent coronavirus disease 2019 (COVID-19). The study aims to investigate the psychological status of pregnant women following the COVID-19 pandemic in Kenya. This descriptive cross-sectional study was conducted amongst 126 pregnant women at the Kenyatta National Hospital, Nairobi. Data on the women’s sociodemographic and obstetric factors, levels of depression, anxiety, and insomnia were recorded on a questionnaire. SPSS version 29 was used to perform paired t-test and multivariate regression analysis to assess for statistically significant association between sociodemographic, obstetrics factors, and related factors on COVID-19-related stressors with depression, anxiety, and insomnia. High levels of depression, anxiety, and insomnia were noted with a mean Edinburgh Postnatal Depression Scale (EPDS) score of 14.90 ± 5.25, State-Trait Anxiety Inventory (STAI) score of 44.12 ± 9.73 and Women’s Health Initiative Insomnia Rating Scale (WHIIRS) score of 10.07 ± 5.81 respectively. There was a strong association between depression, anxiety, and insomnia with specific factors. In conclusion, pregnant women in Kenya experienced significant symptoms of depression, anxiety, and insomnia following the onset of the COVID-19 pandemic.

PMID:40173438 | DOI:10.1080/13548506.2025.2487227

J Med Internet Res. 2025 Apr 2;27:e64028. doi: 10.2196/64028.

ABSTRACT

BACKGROUND: Symptom checker apps (SCAs) are layperson-facing tools that advise on whether and where to seek care, or possible diagnoses. Previous research has primarily focused on evaluating the accuracy, safety, and usability of their recommendations. However, studies examining SCAs’ impact on clinical care, including the patient-physician interaction and satisfaction with care, remain scarce.

OBJECTIVE: This study aims to evaluate the effects of an SCA on satisfaction with the patient-physician interaction in acute care settings. Additionally, we examined its influence on patients’ anxiety and trust in the treating physician.

METHODS: This parallel-group, randomized controlled trial was conducted at 2 emergency departments of an academic medical center and an emergency practice in Berlin, Germany. Low-acuity patients seeking care at these sites were randomly assigned to either self-assess their health complaints using a widely available commercial SCA (Ada Health) before their first encounter with the treating physician or receive usual care. The primary endpoint was patients’ satisfaction with the patient-physician interaction, measured by the Patient Satisfaction Questionnaire (PSQ). The secondary outcomes were patients’ satisfaction with care, their anxiety levels, and physicians’ satisfaction with the patient-physician interaction. We used linear mixed models to assess the statistical significance of primary and secondary outcomes. Exploratory descriptive analyses examined patients’ and physicians’ perceptions of the SCA’s utility and the frequency of patients questioning their physician’s authority.

RESULTS: Between April 11, 2022, and January 25, 2023, we approached 665 patients. A total of 363 patients were included in the intention-to-treat analysis of the primary outcome (intervention: n=173, control: n=190). PSQ scores in the intervention group were similar to those in the control group (mean 78.5, SD 20.0 vs mean 80.8, SD 19.6; estimated difference -2.4, 95% CI -6.3 to 1.1, P=.24). Secondary outcomes, including patients’ and physicians’ satisfaction with care and patient anxiety, showed no significant group differences (all P>.05). Patients in the intervention group were more likely to report that the SCA had a beneficial (66/164, 40.2%) rather than a detrimental (3/164, 1.8%) impact on the patient-physician interaction, with most reporting no effect (95/164, 57.9%). Similar patterns were observed regarding the SCA’s perceived effect on care. In both groups, physicians rarely reported that their authority had been questioned by a patient (intervention: 2/188, 1.1%; control: 4/184, 2.2%). While physicians more often found the SCA helpful rather than unhelpful, the majority indicated it was neither helpful nor unhelpful for the encounter.

CONCLUSIONS: We found no evidence that the SCA improved satisfaction with the patient-physician interaction or care in an acute care setting. By contrast, both patients and their treating physicians predominantly described the SCA’s impact as beneficial. Our study did not identify negative effects of SCA use commonly reported in the literature, such as increased anxiety or diminished trust in health care professionals.

TRIAL REGISTRATION: German Clinical Trial Register DRKS00028598; https://drks.de/search/en/trial/DRKS00028598/entails.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-022-06688-w.

PMID:40173434 | DOI:10.2196/64028

PLoS One. 2025 Apr 2;20(4):e0311459. doi: 10.1371/journal.pone.0311459. eCollection 2025.

ABSTRACT

BACKGROUND/OBJECTIVE: Relative proportion of cases in a multi-strain pandemic like the COVID-19 pandemic provides insight on how fast a newly emergent variant dominates the infected population. However, the behavior of relative proportion of emerging variants is an understudied field. We investigated the emerging behavior of dominant COVID-19 variants using nonlinear statistical methods and calculated the time to dominance of each variant.

METHOD: We used a phenomenological approach to model national- and regional-level variant share data from the national genomic surveillance system provided by the Centers for Disease Control and Prevention to determine the best model to describe the emergence of two recent dominant variants of the SARS-CoV-2 virus: XBB.1.5 and JN.1. The proportions were modeled using logistic, Weibull, and generalized additive models. Model performance was evaluated using the Akaike Information Criteria (AIC) and the root mean square error (RMSE).

FINDINGS: The Weibull model performed the worst out of all three approaches. The generalized additive model approach slightly outperformed the logistic model based on fit statistics, but lacked in interpretability compared to the logistic model. These models were then used to estimate the time elapsed from emergence to dominance in the infected population, denoted by the time to dominance (TTD). All three models yielded similar TTD estimates. The XBB.1.5 variant was found to dominate the population faster compared to the JN.1 variant, especially in HHS Region 2 (New York) where the XBB.1.5 was believed to emerge. This research expounds on how emerging viral strains transition to dominance, informing public health interventions against future emergent COVID-19 variants and other infectious diseases.

PMID:40173404 | DOI:10.1371/journal.pone.0311459

PLoS One. 2025 Apr 2;20(4):e0318825. doi: 10.1371/journal.pone.0318825. eCollection 2025.

ABSTRACT

Understanding past climate is essential to our knowledge of how our current climate system operates, and how it might respond to future change. Techniques to reconstruct climate history are challenging, and both accuracy and certainty are hampered by the quality of the datasets used. Here we both develop a new reconstruction tool and apply it to four ice core proxy based multi-millennial Holocene climate reconstructions, chosen because of their potential influence on East Antarctic climate. The new multi-proxy reconstruction method is called Segmented Linear Integral Correlation Kernel Ensemble Reconstruction (SLICKER). This method employs a segmented linear rather than Gaussian correlation approach and builds an ensemble of reconstructions with a best fit and spread related to the best estimate of uncertainty. This method is robust for non-linear, uneven or differently sampled data and produces high-fidelity reconstructions and associated uncertainty estimates. This new method has the potential to produce more realistic reconstructions, with associated uncertainty estimates based on robust statistical measures that are insensitive to outliers. The main findings from these new reconstructions are: Antarctica temperature shows multi-decadal variability over the last twelve thousand years with increased frequency over the last two thousand years; Zonal Wave 3 index and the Southern Annular Mode both show limited trends over the last two thousand years, but an increase since the 1970s CE; and the Indian Ocean Dipole Moment index has a twentieth century CE upward trend, and a thirteenth to sixteenth century CE below average period which may be related to volcanic activity.

PMID:40173401 | DOI:10.1371/journal.pone.0318825

JMIR Pediatr Parent. 2025 Apr 2;8:e62841. doi: 10.2196/62841.

ABSTRACT

BACKGROUND: Essential Coaching for Every Mother is an SMS text messaging program that positively improved parenting self-efficacy and reduced postpartum anxiety when measured immediately after intervention at 6 weeks postpartum. However, the impact of a short-term postpartum intervention over time is unknown.

OBJECTIVE: This study aims to compare parenting self-efficacy, postpartum anxiety symptoms, postpartum depression symptoms, and perceived social support at 6 months postpartum for mothers in the Essential Coaching for Every Mother trial.

METHODS: Participants (n=150) were randomized to Essential Coaching for Every Mother or control (usual care). Data were collected on parenting self-efficacy (primary outcome, Karitane Parenting Confidence Scale), postpartum anxiety symptoms (Postpartum Specific Anxiety Scale), postpartum depressive symptoms (Edinburgh Postnatal Depression Scale), and perceived social support (Multidimensional Scale of Perceived Social Support) at enrollment and 6-months postpartum. Data were analyzed using analyses of covariance and chi-square analysis.

RESULTS: A total of 139 women completed the primary outcome at 6 months and 136 completed secondary outcomes. At 6 months, there were no statistically significant differences between mothers in the intervention group and mothers in the control group on any of the outcomes. More mothers in the intervention group had higher postpartum anxiety scores (31/68, 45.6%) than mothers in the control group (16/68, 23.5%; P=.007).

CONCLUSIONS: At 6 months postpartum, all mothers had similar scores on parenting self-efficacy, postpartum anxiety symptoms, postpartum depression symptoms, and social support. Thus, Essential Coaching for Every Mother improved parenting self-efficacy and reduced postpartum anxiety at 6 weeks, with all mothers having similar scores at 6 months postpartum.

PMID:40173396 | DOI:10.2196/62841

Asian Pac Isl Nurs J. 2025 Apr 2;9:e63769. doi: 10.2196/63769.

ABSTRACT

BACKGROUND: Racism against Asian Americans escalated during the COVID-19 pandemic. About 31%-91% of Asian American adults and children reported experiencing various types of racism during the pandemic. According to the Federal Bureau of Investigation hate crime statistics, anti-Asian hate crime incidents increased from 158 in 2019 to 279 in 2020 and 746 in 2021. In 2022, the incidents decreased to 499, corresponding to the downward trend of the pandemic. The degree of impact racism has on mental health and wellness among Asian Americans requires investigation, specifically during the COVID-19 pandemic.

OBJECTIVE: We aim to describe racism-related mental health problems experienced by Asian Americans living in the United States and propose implementation strategies for mitigating their consequences.

METHODS: We conducted an integrative review of peer-reviewed publications in English reporting anti-Asian sentiments and racism’s impacts on mental health among Asian Americans in the United States.

RESULTS: The 29 eligible articles report on studies that utilized cross-sectional survey designs with various sample sizes. Racism is directly correlated with the prevalence of depression and anxiety experienced by victims of racist acts. The prevalence of in-person direct racism (racist expression aimed directly at the victim) is lower than in-person indirect racism (racist expression aimed at the ethnic group the victim belongs to). During the COVID-19 pandemic, the incidence of explicit online racism was lower than online indirect racism.

CONCLUSIONS: COVID-19-related racism exacerbated preexisting racism, contributing to worse depression and anxiety among Asian Americans. To address this issue, we propose 2 main approaches: increase public awareness and education about recognizable racist sentiments/acts and systematized reporting of racially motivated crimes to guide political action. At an individual level, culturally responsive, trauma-informed interventions promoting cultural support and cohesion for various Asian American groups will foster this empowerment. These proposed actions will help alleviate racism by reducing stereotypes, empowering victims, and chipping away at the systemic racism structure.

PMID:40173392 | DOI:10.2196/63769

JMIR Med Inform. 2025 Apr 2;13:e66466. doi: 10.2196/66466.

ABSTRACT

BACKGROUND: The use of patient-reported outcome measures (PROMs) is an expected component of high-quality, measurement-based chiropractic care. The largest health care system offering integrated chiropractic care is the Veterans Health Administration (VHA). Challenges limit monitoring PROM use as a care quality metric at a national scale in the VHA. Structured data are unavailable, with PROMs often embedded within clinic text notes as unstructured data requiring time-intensive, peer-conducted chart review for evaluation. Natural language processing (NLP) of clinic text notes is one promising solution to extracting care quality data from unstructured text.

OBJECTIVE: This study aims to test NLP approaches to identify PROMs documented in VHA chiropractic text notes.

METHODS: VHA chiropractic notes from October 1, 2017, to September 30, 2020, were obtained from the VHA Musculoskeletal Diagnosis/Complementary and Integrative Health Cohort. A rule-based NLP model built using medspaCy and spaCy was evaluated on text matching and note categorization tasks. SpaCy was used to build bag-of-words, convoluted neural networks, and ensemble models for note categorization. Performance metrics for each model and task included precision, recall, and F-measure. Cross-validation was used to validate performance metric estimates for the statistical and machine-learning models.

RESULTS: Our sample included 377,213 visit notes from 56,628 patients. The rule-based model performance was good for soft-boundary text-matching (precision=81.1%, recall=96.7%, and F-measure=88.2%) and excellent for note categorization (precision=90.3%, recall=99.5%, and F-measure=94.7%). Cross-validation performance of the statistical and machine learning models for the note categorization task was very good overall, but lower than rule-based model performance. The overall prevalence of PROM documentation was low (17.0%).

CONCLUSIONS: We evaluated multiple NLP methods across a series of tasks, with optimal performance achieved using a rule-based method. By leveraging NLP approaches, we can overcome the challenges posed by unstructured clinical text notes to track documented PROM use. Overall documented use of PROMs in chiropractic notes was low and highlights a potential for quality improvement. This work represents a methodological advancement in the identification and monitoring of documented use of PROMs to ensure consistent, high-quality chiropractic care for veterans.

PMID:40173367 | DOI:10.2196/66466