JMIR Research Protocols最新文献

Background: Streptococcus pneumoniae (Spn) has been a leading cause of bacterial meningitis in children. The most recent estimation of the global burden of Spn meningitis indicates a positive trajectory in eliminating Spn through the implementation of pneumococcal conjugate vaccines. However, continuous monitoring and assessment of the disease burden are necessary due to the evidence of serotype replacement, antibiotic resistance, and the impact of the recent COVID-19 pandemic.

Objective: The aim of this systematic review is to provide an updated and focused assessment of the global and regional burden of Spn meningitis in children, which can guide policies and strategies to reduce the disease burden.

Methods: Population-based studies published from January 1, 2000, to January 1, 2022, were preliminarily searched from the electronic databases PubMed, Embase, Global Health (CABI), and CINAHL Plus without any language restrictions. Studies were included if they reported the incidence, prevalence, mortality, or case-fatality ratio (CFR) for Spn meningitis in children aged 0-4 years; meningitis was confirmed by cerebrospinal fluid culture; the study period was a minimum of 1 year; the number of reported cases was at least 10; and the study had no methodological ambiguities. The article screening process follows the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines. Characteristics including study period, setting, World Health Organization region, income level, vaccination information, and participant data (age, number of cases, deaths, sequelae, and risk factors) will be extracted from the included studies. Search results will be updated and incorporated into our review prior to finalizing the extraction of data. Generalized linear mixed models meta-analysis will be performed to estimate the pooled incidence and CFR. We will further assess the risk of bias and heterogeneity, and will perform subgroup and sensitivity analyses to provide a meaningful interpretation of the current burden and literature for pneumococcal meningitis.

Results: Our preliminary search in December 2021 yielded 9295 articles. Out of 275 studies that were assessed with our eligibility criteria, 117 articles were included. Data extraction and analysis are expected to be complete by January 2025. We plan to publish the results from the full study, including an updated search in 2024, by March 2025.

Conclusions: Given that the major burden of Spn meningitis affects children under the age of 5 years, this systematic review will provide a thorough understanding of the global burden of Spn meningitis in this vulnerable population over a span of 2 decades. Insights into incidence trends, geospatial distribution, risk factors, and sequelae will be valuable for stakeholders, policy makers, and the academic community. This information will ai

Background: Prostate cancer is the most common cancer in men and represents a major problem of public health. The current method of diagnosing/screening for prostate cancer is invasive and costly. There have been renewed and innovative studies on searching urinary biomarker for prostate cancer diagnosis, especially with the technologies of urinary exosomes. However, the technologies of urine exosomes usually need expensive machines such as ultracentrifuge and they are difficult to standardization, which hinder their application in clinical laboratory.

Objective: In this study, our objective is to detect urinary exosomes from prostate cancer for the development of a test to aid in the diagnosis of prostate cancer.

Methods: The exosomes from a prostate cancer cell line LNCaP was used to set up the techniques. For the analysis of urine samples of patients, the methods include the collection of first-void urine by using Colli-Pee device, the isolation of urine exosome by optimized precipitation method and the detection of exosomal PSA by Elecsys® total PSA. We have modified and optimized the isolation of urinary exosomes with precipitation method.

Results: By using the exosomes from the prostate cancer cell line, we have found that the urinary exosomal PSA can be quantified by automatic technique of Elecsys® total PSA. It will be a 2-year study. We shall start to include the patients and controls in the summer of 2024. We expect the results to be published in the last quarter of 2026.

Conclusions: This is the first study to detect the urinary exosomal PSA by the technique of Elecsys® total PSA for the diagnosis of prostate cancer. This study emphasizes on the techniques suitable for the implementation in clinical laboratory, which will facilitate application of urinary exosomes to simplify and improve the diagnosis/screening of prostate cancer.

Background: Screen use time has increased in the past decade owing to the increased availability and accessibility of digital devices and the internet. Several studies have shown an association between increased screen use time and mental health issues such as anxiety and depression. However, studies in the young adult population-a demographic with high screen use-and in low- and middle-income country settings are limited.

Objective: This protocol describes a study that aims to measure self-reported screen use times and patterns in young adults (18-24 y) in India and assess if increased screen use time is associated with poorer mental well-being.

Methods: This protocol describes a cross-sectional study of a pan-India, web-based convenience sample of young adults (18-24 y) with access to digital devices with a screen and a minimum of secondary school education. Participants will be recruited through people in the professional networks of the investigators, which includes pediatricians. The survey will also be distributed via the social media pages of our organization (X [X Corp], Instagram [Meta], Facebook [Meta], etc). Sociodemographic details will be collected through a questionnaire designed by the authors; screen use time and patterns will be assessed using an adaptation of the Screen Time Questionnaire to include data on different apps and websites used on digital devices; and mental health parameters will be gauged using the Warwick-Edinburgh Mental Well-Being Scale, Generalized Anxiety Disorder Scale, Perceived Stress Scale, and Patient Health Questionnaire. For statistical analysis, we will consider the following variables: (1) the primary independent variable is screen use time; (2) other independent variables include age, gender, residence: rural or urban, educational qualifications, employment status, stress associated with familial financial status, average sleep time, number of people living in a house or rooms in that house, BMI, substance use, and past psychiatric history; and (3) dependent variables include mental well-being, depression, anxiety, and perceived stress. To quantify the association between screen use time and mental health, we will perform a Bayesian multivariate multiple regression analysis that models the possibility of multiple alternative hypotheses while accounting for relevant sociodemographic covariables.

Results: The survey instrument has been designed, and feedback has been obtained from the domain experts and members of our organization whose profile is similar to the potential study participants. The final data received after this study has been conducted will be analyzed and shared. As of January 2023, we have not yet initiated the data collection.

Conclusions: Based on the findings of this study, we will be able to establish a correlation between device- and use-specific screen use time and vari

Background: Mental health problems among workers cause enormous losses to companies in Japan. However, workers have been considered to have limited access to psychological support because of time constraints, which makes it difficult for them to engage in face-to-face psychological support interventions.

Objective: This study aimed to present an intervention protocol that describes a randomized controlled trial to examine whether brief guided mindfulness meditation (MM) or self-compassion meditation (SCM) provided by a smartphone app is effective for mental health and work-related outcomes among workers.

Methods: This is an open-label, 3-arm randomized controlled trial. The participants will be recruited through an open call on relevant websites with the following inclusion criteria: (1) employees who are working more than 20 hours per week, (2) between the ages of 18 and 54 years, (3) not on a leave of absence, (4) not business owners or students, and (5) not currently diagnosed with a mental disorder and have a Kessler Psychological Distress Scale-6 score below 13 points. We will include 200 participants and randomly assign them to an SCM course (n=67), an MM course (n=67), and a waitlist group (n=66). The intervention groups (SCM and MM) will be instructed to engage in daily guided self-help, self-compassion, and MMs lasting 6-12 minutes per day over 4 weeks. Primary outcomes will include psychological distress and job performance, and secondary outcomes will include somatic symptoms, cognitive flexibility, self-esteem, self-compassion, perceived stress, well-being, emotion regulation, work engagement, anger, psychological safety, and creativity. All procedures were approved by the ethics committee of the University of Tokyo (22-326). All participants will be informed of the study via the websites, and written informed consent will be collected via web-based forms.

Results: The recruitment of participants began in December 2022, and the intervention began in January 2023. As of September 2023, a total of 375 participants have been enrolled. The intervention and data collection were completed in late October 2023.

Conclusions: This study will contribute to the development of effective self-care intervention content that will improve mental health, work performance, and related outcomes and promote mindful and self-compassionate attitudes when faced with distress.

Trial registration: University Hospital Medical Information Network Clinical Trials Registry UMIN000049466; https://tinyurl.com/23x8m8nf.

International registered report identifier (irrid): DERR1-10.2196/53541.

Background: The reporting of adverse events (AEs) relating to medical devices is a long-standing area of concern, with suboptimal reporting due to a range of factors including a failure to recognize the association of AEs with medical devices, lack of knowledge of how to report AEs, and a general culture of nonreporting. The introduction of artificial intelligence as a medical device (AIaMD) requires a robust safety monitoring environment that recognizes both generic risks of a medical device and some of the increasingly recognized risks of AIaMD (such as algorithmic bias). There is an urgent need to understand the limitations of current AE reporting systems and explore potential mechanisms for how AEs could be detected, attributed, and reported with a view to improving the early detection of safety signals.

Objective: The systematic review outlined in this protocol aims to yield insights into the frequency and severity of AEs while characterizing the events using existing regulatory guidance.

Methods: Publicly accessible AE databases will be searched to identify AE reports for AIaMD. Scoping searches have identified 3 regulatory territories for which public access to AE reports is provided: the United States, the United Kingdom, and Australia. AEs will be included for analysis if an artificial intelligence (AI) medical device is involved. Software as a medical device without AI is not within the scope of this review. Data extraction will be conducted using a data extraction tool designed for this review and will be done independently by AUK and a second reviewer. Descriptive analysis will be conducted to identify the types of AEs being reported, and their frequency, for different types of AIaMD. AEs will be analyzed and characterized according to existing regulatory guidance.

Results: Scoping searches are being conducted with screening to begin in April 2024. Data extraction and synthesis will commence in May 2024, with planned completion by August 2024. The review will highlight the types of AEs being reported for different types of AI medical devices and where the gaps are. It is anticipated that there will be particularly low rates of reporting for indirect harms associated with AIaMD.

Conclusions: To our knowledge, this will be the first systematic review of 3 different regulatory sources reporting AEs associated with AIaMD. The review will focus on real-world evidence, which brings certain limitations, compounded by the opacity of regulatory databases generally. The review will outline the characteristics and frequency of AEs reported for AIaMD and help regulators and policy makers to continue developing robust safety monitoring processes.

International registered report identifier (irrid): PRR1-10.2196/48156.

Background: Women of reproductive age experience cyclical variation in the female sex steroid hormones 17β-estradiol and progesterone during the menstrual cycle that is attenuated by some hormonal contraceptives. Estrogens perform a primary function in sexual development and reproduction but have nonreproductive effects on bone, muscle, and sinew tissues (ie, ligaments and tendons), which may influence injury risk and physical performance.

Objective: The purpose of the study is to understand the effect of the menstrual cycle and hormonal contraceptive use on bone and calcium metabolism, and musculoskeletal health and performance.

Methods: A total of 5 cohorts of physically active women (aged 18-40 years) will be recruited to participate: eumenorrheic, nonhormonal contraceptive users (n=20); combined oral contraceptive pill (COCP) users (n=20); hormonal implant users (n=20); hormonal intrauterine system users (n=20); and hormonal injection users (n=20). Participants must have been using the COCP and implant for at least 1 year and the intrauterine system and injection for at least 2 years. First-void urine samples and fasted blood samples will be collected for biochemical analysis of calcium and bone metabolism, hormones, and metabolic markers. Knee extensor and flexor strength will be measured using an isometric dynamometer, and lower limb tendon and stiffness, tone, and elasticity will be measured using a Myoton device. Functional movement will be assessed using a single-leg drop to assess the frontal plane projection angle and the qualitative assessment of single leg loading. Bone density and macro- and microstructure will be measured using ultrasound, dual-energy x-ray absorptiometry, and high-resolution peripheral quantitative computed tomography. Skeletal material properties will be estimated from reference point indentation, performed on the flat surface of the medial tibia diaphysis. Body composition will be assessed by dual-energy x-ray absorptiometry. The differences in outcome measures between the hormonal contraceptive groups will be analyzed in a one-way between-group analysis of covariance. Within the eumenorrheic group, the influence of the menstrual cycle on outcome measures will be assessed using a linear mixed effects model. Within the COCP group, differences across 2 time points will be analyzed using the paired-samples 2-tailed t test.

Results: The research was funded in January 2020, and data collection started in January 2022, with a projected data collection completion date of August 2024. The number of participants who have consented at the point of manuscript submission is 66. It is expected that all data analysis will be completed and results published by the end of 2024.

Conclusions: Understanding the effects of the menstrual cycle and hormonal contraception on musculoskeletal health and performance will inform

Background: Social communication is a key factor in maintaining cognitive function and contributes to well-being in later life.

Objective: This study will examine the effects of "Photo-Integrated Conversation Moderated by Application version 2" (PICMOA-2), which is a web-based conversational intervention, on cognitive performance, frailty, and social and psychological indicators among community-dwelling older adults.

Methods: This study is a randomized controlled trial with an open-label, 2-parallel group trial and 1:1 allocation design. Community dwellers aged 65 years and older were enrolled in the trial and divided into the intervention and control groups. The intervention group receives the PICMOA-2 program, a web-based group conversation, once every 2 weeks for 6 months. The primary outcome is verbal fluency, including phonemic and semantic fluency. The secondary outcomes are other neuropsychiatric batteries, including the Mini-Mental State Examination, Logical Memory (immediate and delay), verbal paired associates, and comprehensive functional status evaluated by questionnaires, including frailty, social status, and well-being. The effect of the intervention will be examined using a mixed linear model. As a secondary aim, we will test whether the intervention effects vary with the covariates at baseline to examine the effective target attributes.

Results: Recruitment was completed in July 2023. A total of 66 participants were randomly allocated to intervention or control groups. As of January 1, 2024, the intervention is ongoing. Participants are expected to complete the intervention at the end of February 2024, and the postintervention evaluation will be conducted in March 2024.

Conclusions: This protocol outlines the randomized controlled trial study design evaluating the effect of a 6-month intervention with PICMOA-2. This study will provide evidence on the effectiveness of social interventions on cognitive function and identify effective target images for remote social intervention.

Trial registration: UMIN Clinical Trials UMIN000050877; https://tinyurl.com/5eahsy66.

International registered report identifier (irrid): DERR1-10.2196/56608.

Background: Pediatric asthma is a heterogeneous disease; however, current characterizations of its subtypes are limited. Machine learning (ML) methods are well-suited for identifying subtypes. In particular, deep neural networks can learn patient representations by leveraging longitudinal information captured in electronic health records (EHRs) while considering future outcomes. However, the traditional approach for subtype analysis requires large amounts of EHR data, which may contain protected health information causing potential concerns regarding patient privacy. Federated learning is the key technology to address privacy concerns while preserving the accuracy and performance of ML algorithms. Federated learning could enable multisite development and implementation of ML algorithms to facilitate the translation of artificial intelligence into clinical practice.

Objective: The aim of this study is to develop a research protocol for implementation of federated ML across a large clinical research network to identify and discover pediatric asthma subtypes and their progression over time.

Methods: This mixed methods study uses data and clinicians from the OneFlorida+ clinical research network, which is a large regional network covering linked and longitudinal patient-level real-world data (RWD) of over 20 million patients from Florida, Georgia, and Alabama in the United States. To characterize the subtypes, we will use OneFlorida+ data from 2011 to 2023 and develop a research-grade pediatric asthma computable phenotype and clinical natural language processing pipeline to identify pediatric patients with asthma aged 2-18 years. We will then apply federated learning to characterize pediatric asthma subtypes and their temporal progression. Using the Promoting Action on Research Implementation in Health Services framework, we will conduct focus groups with practicing pediatric asthma clinicians within the OneFlorida+ network to investigate the clinical utility of the subtypes. With a user-centered design, we will create prototypes to visualize the subtypes in the EHR to best assist with the clinical management of children with asthma.

Results: OneFlorida+ data from 2011 to 2023 have been collected for 411,628 patients aged 2-18 years along with 11,156,148 clinical notes. We expect to complete the computable phenotyping within the first year of the project, followed by subtyping during the second and third years, and then will perform the focus groups and establish the user-centered design in the fourth and fifth years of the project.

Conclusions: Pediatric asthma subtypes incorporating RWD from diverse populations could improve patient outcomes by moving the field closer to precision pediatric asthma care. Our privacy-preserving federated learning methodology and qualitative implementation work will address several challenges of applying ML