Tag: pubmed

Injury. 2024 Nov 19;56(2):112033. doi: 10.1016/j.injury.2024.112033. Online ahead of print.

ABSTRACT

OBJECTIVE: To investigate the prevalence and influencing factors of acute stress disorder (ASD) and post-traumatic stress disorder (PTSD) in trauma patients, and to explore the predictive effect of ASD on PTSD.

METHODS: A prospective study was conducted on patients hospitalized due to injuries. The first survey used the ASD scale to assess the occurrence of ASD. In one month and three months of follow-up after injury, patients were assessed for the occurrence of PTSD by using the PTSD checklist-civilian version.

RESULTS: The prevalence rates of ASD, one-month PTSD, and three-month PTSD in trauma inpatients were 20.7%, 19.5%, and 17.6%, respectively. ASD is a strong predictor of PTSD, and combining it with severe injury and critical illness can improve the sensitivity and positive predictive ability of predicting the occurrence of PTSD (AUC_Max: 0.827). The important predictive factor for the diagnosis of PTSD is the high alert symptom group of ASD. Moreover, the analysis results showed that the season of trauma happened, comatose state, fear state, psychological burden, and pain intensity were the influencing factors for ASD (P<0.05), while critical illness during hospitalization, psychological burden, and pain intensity were the influencing factors for PTSD (P<0.05).

LIMITATIONS: Some patients with minor and extremely serious injuries were overlooked or missed, resulting in selection bias and information bias that could not be completely avoided.

CONCLUSION: Both trauma conditions and clinical features may affect the occurrence of ASD and PTSD in trauma patients. If ASD in trauma patients is identified early and targeted interventions, it may reduce the occurrence and development of PTSD.

PMID:39602847 | DOI:10.1016/j.injury.2024.112033

JMIR Serious Games. 2024 Nov 27;12:e56918. doi: 10.2196/56918.

ABSTRACT

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is one of the common neurodevelopmental disorders in children and virtual reality (VR) has been used in the diagnosis and treatment of ADHD.

OBJECTIVE: This paper aims to systematically evaluate the effect of VR technology on the attention and motor ability of children with ADHD.

METHODS: The intervention method of the experimental group was VR technology, while the control group adopted non-VR technology. The population was children with ADHD. The outcome indicators were attention and motor abilities. The experimental design was randomized controlled trial. Two researchers independently searched PubMed, Cochrane Library, Web of Science, and Embase for randomized controlled trials related to the effect of VR technology on ADHD children’s attention and motor ability. The retrieval date was from the establishment of each database to January 4, 2023. The PEDro scale was used to evaluate the quality of the included literature. Stata (version 17.0; StataCorp LLC) was used for effect size combination, forest map-making, subgroup analyses, sensitivity analyses, and publication bias. GRADEpro (McMaster University and Evidence Prime Inc) was used to evaluate the level of evidence quality.

RESULTS: A total of 9 literature involving 370 children with ADHD were included. VR technology can improve ADHD children’s attention (Cohen d=-0.68, 95% CI -1.12 to -0.24; P<.001) and motor ability (Cohen d=0.48, 95% CI 0.16-0.80; P<.001). The intervention method and diagnosis type for VR technology had a moderating effect on the intervention’ impact on children’s attention (P<.05). The improvement in children’s attention by “immersive” VR technology was statistically significant (Cohen d=-1.05, 95% CI -1.76 to -0.34; P=.004). The improvement of children’s attention by “nonimmersive” VR technology was statistically significant (Cohen d=-0.28, 95% CI -0.55 to -0.01; P=.04). VR technology had beneficial effects on both children with an “informal diagnosis” (Cohen d=-1.47, 95% CI -2.35 to -0.59; P=.001) and those with a “formal diagnosis” (Cohen d=-0.44, 95% CI -0.85 to -0.03; P=.03).

CONCLUSIONS: VR technology can improve attention and motor ability in children with ADHD. Immersive VR technology has the best attention improvement effect for informally diagnosed children with ADHD.https://www.crd.york.ac.uk/PROSPERO/.

PMID:39602820 | DOI:10.2196/56918

J Am Acad Orthop Surg. 2024 Nov 22. doi: 10.5435/JAAOS-D-24-00469. Online ahead of print.

ABSTRACT

INTRODUCTION: Orthopaedic surgery has been recognized as one of the least diverse surgical specialties. Previous studies have demonstrated that women are heavily underrepresented within orthopaedic surgery. The purpose of this study was to determine whether orthopaedic surgery residency programs with a higher presence of women faculty had a higher proportion of women residents.

METHODS: The Fellowship and Residency Electronic Interactive Database was used to identify all orthopaedic surgery residency programs in the United States. Resident and faculty’s sex and degree were recorded in addition to faculty administrative title (eg, program director, chair) and academic rank (clinician, professor, etc). Pearson correlation coefficients were used to compare the number of women residents with the number of women faculty.

RESULTS: A total of 192 orthopaedic surgery programs were analyzed. Of the 5,747 faculty members and 4,268 residents identified, 13.1% (n = 752) and 22.6% (n = 963) were women, respectively. The number of women residents markedly correlated with the number of women faculty in leadership positions (r = 0.516, P < 0.001), such as chief or chair. The most significant correlations were among women with the academic role of “professor” (r = 0.575, P < 0.001), “assistant professor” (r = 0.555, P < 0.001), and women who held faculty positions but held no higher academic appointment (r = 0.509, P < 0.001). Program directors and assistant program directors were not found to have significant correlations with the number of women residents.

CONCLUSION: This study demonstrates a positive correlation between women faculty and residents at orthopaedic surgery residencies. Some academic positions, such as division chief, held more significant associations, whereas other positions, such as professor emeritus, were not held by any women, thereby limiting statistical analysis. Further investigation into minority representation in orthopaedic surgery and initiatives to address the observed disparities is paramount.

PMID:39602816 | DOI:10.5435/JAAOS-D-24-00469

J Med Internet Res. 2024 Nov 27;26:e55185. doi: 10.2196/55185.

ABSTRACT

BACKGROUND: Many patients do not receive a comprehensive medication reconciliation, mostly owing to limited resources. We hence need an approach to identify those patients at the emergency department (ED) who are at increased risk for clinically relevant discrepancies.

OBJECTIVE: The aim of our study was to develop and externally validate a prediction model to identify patients at risk for at least 1 clinically relevant medication discrepancy upon ED presentation.

METHODS: A prospective, multicenter, observational study was conducted at the University Hospitals Leuven and General Hospital Sint-Jan Brugge-Oostende AV, Belgium. Medication histories were obtained from patients admitted to the ED between November 2017 and May 2022, and clinically relevant medication discrepancies were identified. Three distinct datasets were created for model development, temporal external validation, and geographic external validation. Multivariable logistic regression with backward stepwise selection was used to select the final model. The presence of at least 1 clinically relevant discrepancy was the dependent variable. The model was evaluated by measuring calibration, discrimination, classification, and net benefit.

RESULTS: We included 824, 350, and 119 patients in the development, temporal validation, and geographic validation dataset, respectively. The final model contained 8 predictors, for example, age, residence before admission, number of drugs, and number of drugs of certain drug classes based on Anatomical Therapeutic Chemical coding. Temporal validation showed excellent calibration with a slope of 1.09 and an intercept of 0.18. Discrimination was moderate with a c-index (concordance index) of 0.67 (95% CI 0.61-0.73). In the geographic validation dataset, the calibration slope and intercept were 1.35 and 0.83, respectively, and the c-index was 0.68 (95% CI 0.58-0.78). The model showed net benefit over a range of clinically reasonable threshold probabilities and outperformed other selection criteria.

CONCLUSIONS: Our software-implemented prediction model shows moderate performance, outperforming random or typical selection criteria for medication reconciliation. Depending on available resources, the probability threshold can be customized to increase either the specificity or the sensitivity of the model.

PMID:39602806 | DOI:10.2196/55185

JMIR Ment Health. 2024 Nov 27;11:e59145. doi: 10.2196/59145.

ABSTRACT

BACKGROUND: The need for scalable solutions facilitating access to eating disorder (ED) treatment services that are efficient, effective, and inclusive is a major public health priority. Remote access to synchronous and asynchronous support delivered via health apps has shown promise, but results are so far mixed, and there are limited data on whether apps can enhance health care utilization.

OBJECTIVE: This study aims to examine the effects of app-augmented treatment on clinical outcomes and health care utilization for patients receiving treatment for an ED in outpatient and intensive outpatient levels of care.

METHODS: Recovery Record was implemented in outpatient and intensive outpatient services in a California-based health maintenance organization. We examined outcomes for eligible patients with ED by comparing clinical and service utilization medical record data over a 6-month period after implementation with analogous data for the control group in the year prior. We used a logistic regression model and inverse-weighted estimates of the probability of treatment to adjust for treatment selection bias.

RESULTS: App-augmented treatment was associated with a significant decrease in emergency department visits (P<.001) and a significant increase in outpatient treatment utilization (P<.001). There was a significantly larger weight gain for patients in low-weight categories (ie, underweight, those with anorexia, or those with severe anorexia) with app-augmented treatment (treatment effect: 0.74, 0.25, and 0.35, respectively; P=.02), with a greater percentage of patients moving into a higher BMI class (P=.01).

CONCLUSIONS: Integrating remote patient engagement apps into ED treatment plans can have beneficial effects on both clinical outcomes and service utilization. More research should be undertaken on long-term efficacy and cost-effectiveness to further explore the impact of digital health interventions in ED care.

PMID:39602804 | DOI:10.2196/59145

JMIR Res Protoc. 2024 Nov 27;13:e59545. doi: 10.2196/59545.

ABSTRACT

BACKGROUND: Rapid global population growth and urbanization have led to an increase in urban populations in low- and middle-income countries. Although these urban areas have generally better health outcomes than lower-income rural areas, many environmental, social, and health challenges remain. Vientiane, the capital of Lao People’s Democratic Republic (Lao PDR), has approximately 1.5 of the 7.5 million Laotian population (2022) and provides a unique opportunity to examine health outcomes among socioeconomically diverse populations in the rapidly urbanizing context of the country.

OBJECTIVE: The aim of the Vientiane multigenerational birth cohort (VITERBI) project is to (1) establish a multigenerational birth cohort in Vientiane capital, Lao PDR, which is representative of the local population, (2) serve as the basis for additional observational (ie, cross-sectional) and intervention studies that promote population health in Vientiane province, and (3) investigate the social, epidemiological, and medical problems of public health importance to Lao PDR.

METHODS: VITERBI is a prospective multigenerational birth cohort. The study population is structured around children born between July 1, 2022, and June 30, 2023, who reside in Chanthabuly, Sikhottabong, Sangthong, or Mayparkngum districts of Vientiane. Whenever possible, children and their mothers are enrolled during pregnancy; nonreported pregnancies are enrolled after birth. The cohort plans to enroll 3000 pregnant women and their children and the infants’ fathers, grandparents, and great-grandparents for a total study population of approximately 13,000 individuals. Participants will be followed throughout the life course with a range of data collected, including demographics, behavior, diet, physical activity, physiology, neurodevelopment, health history, quality of life, environmental exposures, depression, anxiety, stress, resilience, household characteristics, obstetric history, birth outcomes, and various living and dementia scales for older adults. Biomarkers collected include height, weight, blood pressure, and hemoglobin levels. Currently, no statistical analyses are planned.

RESULTS: As of April 2024, this study has enrolled 3500 pregnant women and 4579 family members. Study participation is ongoing until May 2025 at minimum, with the goal to extend follow-up until 2050.

CONCLUSIONS: The study cohort will be used as a basis for further observational (cross-sectional, longitudinal) and intervention studies. It also serves as a tool to investigate social, epidemiological, and medical problems of public health importance to Lao PDR, which will contribute to broader understanding of regional and international contexts.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59545.

PMID:39602796 | DOI:10.2196/59545

J Med Internet Res. 2024 Nov 27;26:e54248. doi: 10.2196/54248.

ABSTRACT

BACKGROUND: Detecting early dropout from digital interventions is crucial for developing strategies to enhance user retention and improve health-related behavioral outcomes. Bricker and colleagues proposed a single metric that accurately predicted early dropout from 4 digital tobacco cessation interventions based on log-in data in the initial week after registration. Generalization of this method to additional interventions and modalities would strengthen confidence in the approach and facilitate additional research drawing on it to increase user retention.

OBJECTIVE: This study had two research questions (RQ): RQ1-can the study by Bricker and colleagues be replicated using data from a large-scale observational, multimodal intervention to predict early dropout? and RQ2-can first-week engagement patterns identify users at the greatest risk for early dropout, to inform development of potential “rescue” interventions?

METHODS: Data from web users were drawn from EX, a freely available, multimodal digital intervention for tobacco cessation (N=70,265). First-week engagement was operationalized as any website page views or SMS text message responses within 1 week after registration. Early dropout was defined as having no subsequent engagement after that initial week through 1 year. First, a multivariate regression model was used to predict early dropout. Model predictors were dichotomous measures of engagement in each of the initial 6 days (days 2-7) following registration (day 1). Next, 6 univariate regression models were compared in terms of their discrimination ability to predict early dropout. The sole predictor of each model was a dichotomous measure of whether users had reengaged with the intervention by a particular day of the first week (calculated separately for each of 2-7 days).

RESULTS: For RQ1, the area under the receiver operating characteristic curve (AUC) of the multivariate model in predicting dropout after 1 week was 0.72 (95% CI 0.71-0.73), which was within the range of AUC metrics found in the study by Bricker and colleagues. For RQ2, the AUCs of the univariate models increased with each successive day until day 4 (0.66, 95% CI 0.65-0.67). The sensitivity of the models decreased (range 0.79-0.59) and the specificity increased (range 0.48-0.73) with each successive day.

CONCLUSIONS: This study provides independent validation of the use of first-week engagement to predict early dropout, demonstrating that the method generalizes across intervention modalities and engagement metrics. As digital intervention researchers continue to address the challenges of low engagement and early dropout, these results suggest that first-week engagement is a useful construct with predictive validity that is robust across interventions and definitions. Future research should explore the applicability and efficiency of this model to develop interventions to increase retention and improve health behavioral outcomes.

PMID:39602788 | DOI:10.2196/54248

J Med Internet Res. 2024 Nov 27;26:e60590. doi: 10.2196/60590.

ABSTRACT

BACKGROUND: Teledentistry is a field of activities that comprises information and communication technologies (ICTs) applied to dentistry, including the exchange of clinical information, patient care, and the use of educational strategies across remote distances. Its use has grown progressively over the past decades-intensified by the COVID-19 pandemic-and has been improving the provision of dental services and educational strategies ever since.

OBJECTIVE: This evidence gap map (EGM) study aims to present a collection of systematic reviews (SRs) with meta-analyses to answer the question “What are the applications of teledentistry in dental services and dental education?” by identifying gaps and current evidence on the improvement of health care and education.

METHODS: The EGM methodology has been developed by the Latin American and Caribbean Center on Health Sciences Information and is based on the concept created by the International Initiative for Impact Evaluation. Embase, PubMed, and Virtual Health Library databases were used for the literature research, using terms for teledentistry associated with eHealth, dental education, and oral health care. The data obtained from the included studies were then characterized in a Microsoft Excel spreadsheet, with a matrix containing 8 intervention groups (combined interventions, e-learning and tele-education, teleconsultation and teleservice, telemonitoring, telediagnosis, telescreening, ICTs, and artificial intelligence) and 8 outcome groups (diagnosis accuracy, education and professional training, user behavior, clinical practice, patient-centered outcomes, clinical outcomes, health services management, and access to health services). The quality of the studies was assessed using AMSTAR2 (A Measurement Tool to Assess Systematic Reviews). The visual analytics platform Tableau (Salesforce) was used to graphically display the confidence level, number of reviews, health outcomes, and intervention effects.

RESULTS: The confidence level obtained by the criteria applied was high for 28% (19/68) of the studies, moderate for 6% (4/68), low for 15% (10/68), and critically low for 51% (35/68). Among the interventions, the ICT group stood out with 182 (36.8%) out of 494 associations, followed by interventions with e-learning and tele-education (n=96, 19.4% of associations), telediagnosis (n=67, 13.6%), and combined interventions (n=53, 10.7%). Most of the outcomes were aimed at education and professional training (97/494, 19.6% of associations), patient-centered outcomes (74/494, 15%), and health services management (60/494, 12.1%).

CONCLUSIONS: This EGM presents an overview of the contributions of teledentistry in patient care, health services, clinical practice, and education. The study results may help guide future research and policy decisions and serve as a convenient virtual tool for accessing valuable evidence-based information on teledentistry.

PMID:39602783 | DOI:10.2196/60590

J Med Internet Res. 2024 Nov 27;26:e56651. doi: 10.2196/56651.

ABSTRACT

BACKGROUND: Online wellness influencers (individuals dispensing unregulated health and wellness advice over social media) may have incentives to oppose traditional medical authorities. Their messaging may decrease the overall effectiveness of public health campaigns during global health crises like the COVID-19 pandemic.

OBJECTIVE: This study aimed to probe how wellness influencers respond to a public health campaign; we examined how a sample of wellness influencers on Twitter (rebranded as X in 2023) identified before the COVID-19 pandemic on Twitter took stances on the COVID-19 vaccine during 2020-2022. We evaluated the prevalence of provaccination messaging among wellness influencers compared with a control group, as well as the rhetorical strategies these influencers used when supporting or opposing vaccination.

METHODS: Following a longitudinal design, wellness influencer accounts were identified on Twitter from a random sample of tweets posted in 2019. Accounts were identified using a combination of topic modeling and hand-annotation for adherence to influencer criteria. Their tweets from 2020-2022 containing vaccine keywords were collected and labeled as pro- or antivaccination stances using a language model. We compared their stances to a control group of noninfluencer accounts that discussed similar health topics before the pandemic using a generalized linear model with mixed effects and a nearest-neighbors classifier. We also used topic modeling to locate key themes in influencer’s pro- and antivaccine messages.

RESULTS: Wellness influencers (n=161) had lower rates of provaccination stances in their on-topic tweets (20%, 614/3045) compared with controls (n=242 accounts, with 42% or 3201/7584 provaccination tweets). Using a generalized linear model of tweet stance with mixed effects to model tweets from the same account, the main effect of the group was significant (β₁=-2.2668, SE=0.2940; P<.001). Covariate analysis suggests an association between antivaccination tweets and accounts representing individuals (β=-0.9591, SE=0.2917; P=.001) but not social network position. A complementary modeling exercise of stance within user accounts showed a significant difference in the proportion of antivaccination users by group (χ²₁[N=321]=36.1, P<.001). While nearly half of the influencer accounts were labeled by a K-nearest neighbor classifier as predominantly antivaccination (48%, 58/120), only 16% of control accounts were labeled this way (33/201). Topic modeling of influencer tweets showed that the most prevalent antivaccination themes were protecting children, guarding against government overreach, and the corruption of the pharmaceutical industry. Provaccination messaging tended to encourage followers to take action or emphasize the efficacy of the vaccine.

CONCLUSIONS: Wellness influencers showed higher rates of vaccine opposition compared with other accounts that participated in health discourse before the pandemic. This pattern supports the theory that unregulated wellness influencers have incentives to resist messaging from establishment authorities such as public health agencies.

PMID:39602782 | DOI:10.2196/56651

Opt Lett. 2024 Dec 1;49(23):6609-6612. doi: 10.1364/OL.538457.

ABSTRACT

The mode-pairing quantum key distribution (MP-QKD) protocol, which can achieve high key rates over long distances without phase locking, is a potential candidate for implementing intercity QKD. However, achieving precise control of the light source intensity in a field MP-QKD experiment is an exceedingly challenging task. In this Letter, we study the decoy-state MP-QKD protocol with light source intensity fluctuations. Furthermore, we propose a statistical analysis method based on the T-distribution to calculate confidence intervals of intensity fluctuations. Finally, in the field MP-QKD experiments, considering intensity fluctuations and the finite size effect, we obtain secure key rates of 1.03 × 10^-6 bit/pair and 3.64 × 10^-6 bit/pair for the symmetric (195.8 km) and asymmetric (127.7 km) cases, respectively.

PMID:39602706 | DOI:10.1364/OL.538457