Stakeholders' Perspectives on Current Issues in Data Monitoring Committees.

Data Monitoring Committees (DMCs) are groups of experts that review accumulating data from one or more ongoing clinical studies and advise the Sponsor regarding the continuing safety of study subjects along with the continuing validity and scientific merit of the study. Although DMCs are widely used, considerable variability exists in their conduct. This paper offers recommendations, derived from sessions given at the 2023 Central European Network International Biometric and Statisticians in the Pharmaceutical Industry Conferences' and the authors' experiences. We focus on four topics that are part of the DMC process and where there is unclarity and inconsistency in current practices: (1) Communication with the DMC-We reflect on the importance of effective, proper communication channels between the DMC and relevant stakeholders to foster collaboration and exchange of critical information while retaining study integrity throughout. (2) Open sessions-We discuss the benefits of incorporating open sessions in DMC meetings to enhance transparency, inclusivity, and the consideration of diverse perspectives, as well as pitfalls of open sessions. (3) Access to efficacy data-We highlight the need for appropriate access to efficacy data by DMCs and discuss how to implement this in practice and how to address potential concerns regarding multiplicity. (4) Interactive data displays-We outline the utilization of interactive data displays to facilitate a more intuitive understanding of study results by the DMC. By addressing these topics, we aim to provide comprehensive practical recommendations that bridge the gap between current practices and optimal DMC functionality.

Global data monitoring systems and early identification for kidney diseases.

BACKGROUND: Data monitoring and surveillance systems are the cornerstone for governance and regulation, planning, and policy development for chronic disease care. Our study aims to evaluate health systems capacity for data monitoring and surveillance for kidney care. METHODS: We leveraged data from the third iteration of the International Society of Nephrology Global Kidney Health Atlas (ISN-GKHA), an international survey of stakeholders (clinicians, policymakers and patient advocates) from 167 countries conducted between July and September 2022. ISN-GKHA contains data on availability and types of kidney registries, the spectrum of their coverage, as well as data on national policies for kidney disease identification. RESULTS: Overall, 167 countries responded to the survey, representing 97.4% of the global population. Information systems in forms of registries for dialysis care were available in 63% (n = 102/162) of countries, followed by kidney transplant registries (58%; n = 94/162), and registries for non-dialysis chronic kidney disease (19%; n = 31/162) and acute kidney injury (9%; n = 14/162). Participation in dialysis registries was mandatory in 57% (n = 58) of countries; however, in more than half of countries in Africa (58%; n = 7), Eastern and Central Europe (67%; n = 10), and South Asia (100%; n = 2), participation was voluntary. The least-reported performance measures in dialysis registries were hospitalization (36%; n = 37) and quality of life (24%; n = 24). CONCLUSIONS: The variability of health information systems and early identification systems for kidney disease across countries and world regions warrants a global framework for prioritizing the development of these systems.

Data Safety Monitoring Boards: Overview of Structure and Role in Spinal Cord Injury Studies.

This paper provides an overview of the history, composition, organization, responsibilities, and regulatory requirements of Data Safety Monitoring Boards (DSMB), with particular reference to the context of clinical trials in spinal cord injury. It is intended to help potential members of such boards and those undertaking the design of new clinical trials to understand the important role of the DSMB in safeguarding the integrity of complex trials, promoting safety, and countering potential bias. An independent DSMB helps to protect research subjects by providing study oversight and serves as an additional step to assure that clinical trials are performed to existing and appropriate standards. The DSMB must meet on a regular schedule, diligently evaluate all the information it receives, and report in a timely and decisive manner. Members must be free of significant conflicts of interest throughout the study and be adequately trained and experienced to serve their roles within the group. DSMB service can be a valuable learning experience and a gratifying opportunity to participate in advancing medicine and helping to maintain and improve the standards of research.

Probiotic fermentation environment control under intelligent data monitoring.

Probiotic fermentation studies are vital in many areas, particularly when it comes to feeding applications. This work examines probiotic fermentation in oil tea crops. The assessment of tea saponin-degrading bacteria and optimization of fermentation conditions using fermented oil tea cake under natural conditions, screening out six strains with strong ability to degrade tea saponin; selection of the best tea saponin degradation strain L.2 and recognition of its morphological features and ITS sequence to obtain L.2 strain is Aspergillus Niger. Oil tea is rich in tea saponin. Aspergillus Niger degraded tea saponins in oil teacakes at a rate of 93.96 % under the ideal conditions of 31.3 oC, 103.5 h, and 4.57 mL of initial acid addition. This has been accomplished via solid-state fermentation of L.2 using single factor studies and surface response optimization experiments. Moreover, Aspergillus Niger degraded tea saponins in oil tea cakes at a rate of 93.96% at the ideal circumstances of 31.3 C, 103.5 h, and 4.57 mL of initial acid addition.

A risk-based monitoring approach to source data monitoring and documenting monitoring findings.

BACKGROUND: Clinical trial monitoring is evolving from labor-intensive to targeted approaches. The traditional 100% Source Data Monitoring (SDM) approach fails to prioritize data by significance, diverting attention from critical elements. Despite regulatory guidance on Risk-Based Monitoring (RBM), its widespread implementation has been slow. METHODS: Our study teams assess the study's overall risk, document heightened and critical risks, and create a study-specific risk-based monitoring plan, integrating SDM and Central Data Monitoring (CDM). SDM combines a fixed list of pre-identified variables and a list of randomly identified variables to monitor. Identifying variables follows a two-step approach: first, a random sample of participants is selected, second, a random set of variables for each participant selected is identified. Sampling weights prioritize critical variables. Regular team meetings are held to discuss and compile significant findings into a Study Monitoring Report. RESULTS: We present a random SDM sample and a Study Monitoring Report. The random SDM output includes a look-up table for selected database elements. The report provides a holistic view of the study issues and overall health. CONCLUSIONS: The proposed random sampling method is used to monitor a representative set of critical variables, while the Study Monitoring Report is written to summarize significant monitoring findings and data trends. The report allows the sponsor to assess the current status of the study and data effectively. Communicating and sharing emerging insights facilitates timely adjustments of future monitoring activities, optimizing efficiencies, and study outcomes.

Measuring the effectiveness of hybrid diabetes care over 90 days through continuous data monitoring in type 2 diabetic patients.

Background: Diabetes Mellitus, a global health challenge, affects 537 million individuals. Traditional management relies on periodic clinic visits, but technological advancements, including remote monitoring, offer transformative changes. Telemedicine enhances access, convenience, adherence, and glycemic control. Challenges include trust-building and limitations in face-to-face interactions. Integrating remote monitoring with in-person healthcare creates a hybrid approach. This study evaluates the impact on Type 2 Diabetes patients over 3 months. Methods: A retrospective case-control observational study. Inclusion criteria involved previous Type 2 Diabetes diagnosis and a minimum 3-month GluCare model period with two physical visits. Patients in the case group had in-clinic visits, bi-weekly app engagement, and monthly body weight readings. Control group had in-clinic visits only. Outcomes measured included HbA1c, lipid profile, CV risk, eGFR, urine Albumin/Creatinine Ratio, Uric Acid, and CRP. Results: Case group showed significant HbA1c improvements (-2.19%), especially in higher baseline levels. Weight, BMI, LDL, total cholesterol, and CVD risk also improved. Controls showed smaller improvements. Higher digital interactions correlated with better outcomes. Patients with ≥11 interactions showed significant reductions in HbA1c (-2.38%) and weight (-6.00 kg). Conclusion: The GluCare.Health hybrid model demonstrates promising outcomes in Type 2 diabetes management. The integration of in-clinic consultations with continuous remote monitoring leads to substantial improvements in glycemic control and clinical parameters. The study highlights the importance of patient engagement in achieving positive outcomes, with higher digital interactions associated with greater reductions in HbA1c and weight. The hybrid approach proves more effective than digital-only interventions, emphasizing the need for comprehensive, end-to-end solutions in diabetes care.

Training the Next Generation of Data Monitoring Committee Members: An Initiative of the Heart Failure Collaboratory.

Clinical trials are vital for assessing therapeutic interventions. The associated data monitoring committees (DMCs) safeguard patient interests and enhance trial integrity, thus promoting timely, reliable evaluations of those interventions. We face an urgent need to recruit and train new DMC members. The HFC (Heart Failure Collaboratory), a multidisciplinary public-private consortium of academics, trialists, patients, industry representatives, and government agencies, is working to improve the clinical trial ecosystem. The HFC aims to improve clinical trial efficiency and quality by standardizing concepts, and to help meet the demand for experienced individuals on DMCs by creating a standardized approach to training new members. This paper discusses the HFC's training workshop, and an apprenticeship model for new DMC members. It describes opportunities and challenges DMCs face, along with common myths and best practices learned through previous experiences, with an emphasis on data confidentiality and need for quality independent statistical reporting groups.

Long-term monitoring data of hygrothermal conditions of a retrofitted historic building in Settequerce, South Tyrol (Italy).

This data collection presents a comprehensive five-year monitoring record from a historic building located in Settequerce, South Tyrol (Italy). The 17th-century building, characterized by a stone masonry, was subject to a retrofit in 2017 that introduced a wood fiber insulation panel to the inner side of the building's walls. As part of the renovation process, a monitoring system was installed. This system incorporated internal, external, and in-wall sensors, which were strategically placed at three different points within the wall to evaluate the effects of the insulation on the stratigraphy. This setup ensured the continuous collection of climatic data, both from the exterior and interior environments, as well as from within the renovated wall's stratigraphy. This comprehensive dataset delivers new insights into the long-term performance of retrofit interventions. The significant potential for reuse of this dataset makes it a substantial foundation for future studies on energy-efficient retrofitting and the preservation of historical structures in comparable climate (Climate E Köppen). It is expected to inform data-driven decisions in future renovation and preservation projects of comparable historic buildings.

An evaluation of confidence intervals for a cumulative proportion to enable decisions at interim reviews of single-arm trials.

BACKGROUND: Clinical trials often include interim analyses of the proportion of participants experiencing an event by a fixed time-point. A pre-specified proportion excluded from a corresponding confidence interval (CI) may lead an independent monitoring committee to recommend stopping the trial. Frequently this cumulative proportion is estimated by the Kaplan-Meier estimator with a Wald approximate CI, which may have coverage issues with small samples. METHODS: We reviewed four alternative CI methods for cumulative proportions (Beta Product Confidence Procedure (BPCP), BPCP Mid P, Rothman-Wilson, Thomas-Grunkemeier) and two CI methods for simple proportions (Clopper-Pearson, Wilson). We conducted a simulation study comparing CI methods across true event proportions for 12 scenarios differentiated by sample sizes and censoring patterns. We re-analyzed interim data from A5340, a HIV cure trial considering the proportion of participants experiencing virologic failure. RESULTS: Our simulation study highlights the lower and upper tail error probabilities for each CI method. Across scenarios, we found differences in the performance of lower versus upper bounds. No single method is always preferred. The upper bound of a Wald approximate CI performed reasonably with some error inflation, whereas the lower bound of the BPCP Mid P method performed well. For a trial design similar to A5340, we recommend BPCP Mid P. CONCLUSIONS: The design of future single-arm interim analyses of event proportions should consider the most appropriate CI method based on the relevant bound, anticipated sample size and event proportion. Our paper summarizes available methods, demonstrates performance in a simulation study, and includes code for implementation.

Training New DMC Members: A Call to Action.

Historically, clinical trialists developed DMC expertise and experience with "on the job training". Clinical trialists have recognized a large and growing gap between the demand and supply of trained DMC members due in part to the huge increase in ongoing clinical trials. A critical need exists to increase the supply of DMC trained clinicians and biostatisticians. Despite a rich collection of published training material, many have recommended a mentoring process to train clinical trialists and data scientists lacking DMC experience. We propose that academic, regulatory, industry and other leaders in clinical trials support including investigators lacking DMC experience as members of a DMC so that they can be trained to learn about the DMC processes during conduct of an actual trial".

Interim decision making in seamless trial designs: An application in an adaptive dose-finding study in a rare kidney disease.

Adaptive seamless trial designs, combining the learning and confirming cycles of drug development in a single trial, have gained popularity in recent years. Adaptations may include dose selection, sample size re-estimation and enrichment of the study population. Despite methodological advances and recognition of the potential efficiency gains such designs offer, their implementation, including how to enable efficient decision making on the adaptations in interim analyzes, remains a key challenge in their adoption. This manuscript uses a case study of an adaptive seamless proof-of-concept (Phase 2a)/dose-finding (Phase 2b) to showcase potential adaptive features that can be implemented in trial designs at earlier development stages and the role of simulations in assessing the design operating characteristics and specifying the decision rules for the adaptations. It further outlines the elements needed to support successful interim analysis decision making on the adaptations while safeguarding study integrity, including the role of different stakeholders, interactive simulation-based tools to facilitate decision making and operational aspects requiring preplanning. The benefits of the adaptive Phase 2a/2b design chosen compared to following the traditional two separate studies (2a and 2b) paradigm are discussed. With careful planning and appreciation of their complexity and components needed for their implementation, seamless adaptive designs have the potential to yield significant savings both in terms of time and resources.

Data Monitoring Committees and clinical trials: From scientific justification to organisation.

Clinical trials often last several months or even several years. As the trial progresses, it can be tempting to find out whether the data obtained already answers the question posed at the start of the trial in order to stop inclusions or monitoring earlier. However, knowing and taking into account interim results can sometimes compromise the integrity of the results, which is counterproductive. To minimise this risk and ensure that the treatments are assessed reliably, safety and/or efficacy criteria are monitored during the study by a Data Monitoring Committee. After receiving the results confidentially, the Data Monitoring Committee assesses the benefit/risk ratio of the study treatment and recommends that the trial be continued, modified or terminated. Data Monitoring Committee members issuing these recommendations have an important responsibility: a hasty decision to end the trial may lead to inconclusive results unable to answer the initial question and, inversely, delaying the decision to end the trial may expose the subjects to potentially ineffective or even harmful interventions. The Data Monitoring Committee's task is therefore particularly complex. With this in mind, the round table discussion at the Giens workshops was a chance to review the scientific justification for creating Data Monitoring Committees and to recall the need for their members to receive comprehensive training on the complexities of multiple analyses, confidentiality requirements applying to the results and the need for them to be aware that recommendations to end a trial must be based on data that is robust enough to assess the benefit/risk ratio of the treatment studied.

Effectiveness of a hybrid technology enabled care model as measured by ICHOM standard set on established and managed type 2 diabetes already using medications: A RWE retrospective study.

Aims: Diabetes is a pressing global health issue, demanding innovative strategies for improved treatment. However, traditional care often falls short of patient goals. To address this, digital health solutions, including smartphone apps and remote monitoring, have emerged as crucial in diabetes management. This study aims to assess a comprehensive intervention, combining remote continuous data monitoring (RCDM) with in-clinic care, for enhancing diabetes-related outcomes. Additionally, it seeks to evaluate the effectiveness of the digital RCDM component by comparing adherent and non-adherent patients. Methods: Conducted in the United Arab Emirates, a retrospective study involved 89 patients primarily on anti-diabetic medications. They were split into two groups based on adherence to RCDM. Over time, significant improvements were observed across various parameters. Results: Notably, patients exhibited weight loss (-4.0 ± 5.3, p < 0.001), reduced waist circumference (-4.74 ± 7.8, p < 0.001), lowered HbA1c levels (-1.00 ± 1.3, p < 0.001), decreased systolic BP (-3.1 ± 13.1, p = 0.035), and diminished diastolic BP (-3.4 ± 9.9, p = 0.002) annually. Furthermore, patients adhering to the GluCare model demonstrated substantial HbA1c reductions (-1.53 ± 1.5, p < 0.001), improved lipid profiles, notably decreased total Cholesterol (-16.6 ± 50.3, p = 0.034), and lowered LDL levels (-18.65 ± 42.6, p = 0.006). Conclusions: The intervention model effectively managed T2D patients through a comprehensive approach, yielding notable improvements in HbA1c levels and other outcomes within a year. The study underscores the limitations of traditional care and reliance simply on pharmacotherapy, and emphasizes the need for a hyper-personalized, and continuous approach for T2D management.

A rapid review of current engagement strategies with people who use drugs in monitoring and reporting on substance use-related harms.

BACKGROUND: The Canadian drug supply has significantly increased in toxicity over the past few years, resulting in the worsening of the overdose crisis. A key initiative implemented during this crisis has been data monitoring and reporting of substance use-related harms (SRH). This literature review aims to: (1) identify strategies used for the meaningful engagement of people who use drugs (PWUD) in local, provincial, and national SRH data system planning, reporting, and action and (2) describe data monitoring and reporting strategies and common indicators of SRH within those systems. METHODS: We searched three academic and five gray literature databases for relevant literature published between 2012 and 2022. Team members who identify as PWUD and a librarian at Public Health Ontario developed search strings collaboratively. Two reviewers screened all search results and applied the eligibility criteria. We used Microsoft Excel for data management. RESULTS: Twenty-two articles met our eligibility criteria (peer-reviewed n = 10 and gray literature reports n = 12); most used qualitative methods and focused on the Canadian context (n = 20). There were few examples of PWUD engaged as authors of reports on SRH monitoring. Among information systems involving PWUD, we found two main strategies: (1) community-based strategies (e.g., word of mouth, through drug sellers, and through satellite workers) and (2) public health-based data monitoring and communication strategies (e.g., communicating drug quality and alerts to PWUD). Substance use-related mortality, hospitalizations, and emergency department visits were the indicators most commonly used in systems of SRH reporting that engaged PWUD. CONCLUSION: This review demonstrates limited engagement of PWUD and silos of activity in existing SRH data monitoring and reporting strategies. Future work is needed to better engage PWUD in these processes in an equitable manner. Building SRH monitoring systems in partnership with PWUD may increase the potential impact of these systems to reduce harms in the community.

What can we learn from severity index on flight data monitoring? Analysis of safety resilience in flight operations during COVID-19 disruptions.

The unexpected spread of the pandemic raised concerns regarding pilots' skill decay resulting from the significant drops in the frequency of flights by about 70%. This research retrieved 4761 Flight Data Monitoring (FDM) occurrences based on the FDM programme containing 123,140 flights operated by an international airline between June 2019 and May 2021. The FDM severity index was analysed by event category, aircraft type, and flight phase. The results demonstrate an increase in severity score from the pre-pandemic level to the pandemic onset on events that occurred on different flight phases. This trend is not present in the third stage, which indicates that pilots and the safety management system of the airline demonstrated resilience to cope with the flight disruptions during the pandemic. Through the analysis of event severity, FDM enables safety managers to recommend measures to increase safety resilience and self-monitoring capabilities of both operators and regulators.Practitioner summary: The onset of the pandemic led to a rise in the severity of flight data monitoring events in a large airline, likely linked to a lack of operational practice and skills decay. This was demonstrated across different flight phases and aircraft types. In the settled pandemic period, the severity index returned to pre-pandemic levels, indicating that the resilience of individual pilots and safety management systems is critical to operational safety.HIGHLIGHTSThe FDM event severity scores significantly increased following the pandemic onset, especially for event categories involving pilot core competencies.The FDM event severity scores stagnated or decreased during the later pandemic stage indicating resilience among the airline pilots and the airline's safety management system.The airline and pilots demonstrated resilience by effectively mitigating the effects of proficiency decay which took place as the pandemic started.FDM analysis has shown to be effective in establishing a proactive SMS programme to mitigate the negative impacts of the pandemic on aviation safety.

Deprioritization of Ongoing Clinical Trials.

To be ethical, clinical trials must exhibit a favorable risk-benefit balance at the time of their initiation. However, in some cases, the expected value of a study decreases while the study is ongoing, due to developments outside of the study itself, such as findings from other studies or an otherwise shifting evidence base. While such situations are acknowledged in the research community, they have not received sufficient attention, given the high costs of uninformative studies, both in material and human capital. In addition, the Covid-19 pandemic has exposed serious shortcomings with current approaches to monitoring studies for continued relevance and value. In this article, with reference to a case study from the Covid-19 pandemic, we identify and describe the importance and challenge of ensuring that clinical trials continue to exhibit scientific relevance and value once initiated. We explore the ethical dynamics of these situations and identify unresolved issues. While more empirical work is needed to ensure that proposed solutions to the issues are evidence based, we offer some provisional considerations that amount to a framework for approaching these challenging situations.

Monitoring of electricity generation from exhaust waste heat and wireless data recording from a mobile phone in real driving conditions of a vehicle.

In this study, a system is designed to generate electrical energy from the exhaust waste heat of vehicles using a thermoelectric generator. Electronic hardware that can communicate wirelessly, firmware, and mobile software specific to the system have been developed to control and monitor this system. The system comprises hexagonal aluminum components, thermoelectric generators, a cooler, sensors, software, and electronic hardware. The easily removable hexagonal modular aluminum component is designed to transmit heat from the exhaust pipe to thermoelectric generators. It used a thermoelectric generator (TEG-SP1848) on each edge of this hexagonal component and a heatsink to cool the generator. The voltage and current values of the electrical energy produced in the observations made under real driving conditions are recorded on the SD card on the system. In addition, system-specific mobile software has been developed by the work team. With this software, the system can be controlled, as well as visualizing the instantaneous parameters of the system. According to the results obtained from the test drives, electrical energy was obtained at a maximum voltage of 9.8 V and a current of 0.32 A. This electrical energy from the exhaust waste heat can be stored in the vehicle's existing battery. In this way, since the alternator used for the vehicle's electricity generation will be activated less, fuel savings will be achieved in the engine, and harmful exhaust emissions will be reduced. In addition, the electrical energy obtained by this method can be stored in an external battery independent of the vehicle battery and used for various purposes. In contrast, the vehicle is stationary or has a portable battery.

Integrating Wearable Sensors and Machine Learning for the Detection of Critical Events in Industry Workers.

The event where an industry worker experiences some sort of critical health problems on site, due to factors not strictly related to the job, poses a serious concern and is an issue of research. These events can be mitigated almost entirely if the workers' health is being monitored in real time by an occupational physician along with an artificial intelligence system that can foresee a health incident and act fast and efficiently. For this reason, we developed a framework of devices, systems, and algorithms which help the industry workers along with the industries to monitor such events and, if possible, minimize them. The aforementioned framework performs seamlessly and autonomously and creates a system where the health of the industry workers is being monitored in real time. In the proposed solution, the worker would wear a wrist sensor in the form of a smartwatch as well as a blood pressure device on the ear. These sensors can communicate directly with a cloud storage system to store sensor data, and then real-time data analysis can be performed. Subsequently, all results can be displayed in an interface operated by an occupational physician, and in case of a health issue event, the doctor and the worker will be notified.

Data monitoring committees in pediatric randomized controlled trials registered in ClinicalTrials.gov.

BACKGROUND: Data monitoring committees advise on clinical trial conduct through appraisal of emerging data to ensure participant safety and scientific integrity. While consideration of their use is recommended for trials performed with vulnerable populations, previous research has shown that data monitoring committees are reported infrequently in publications of pediatric randomized controlled trials. We aimed to assess the frequency of reported data monitoring committee adoption in ClinicalTrials.gov registry records and to examine the influence of key trial characteristics. METHODS: We conducted a cross-sectional data analysis of all randomized controlled trials performed exclusively in a pediatric population and registered in ClinicalTrials.gov between 2008 and 2021. We used the Access to Aggregate Content of ClinicalTrials.gov database to retrieve publicly available information on trial characteristics and data on safety results. Abstracted data included reported trial design and conduct parameters, population and intervention characteristics, reasons for prematurely halting, serious adverse events, and mortality outcomes. We performed descriptive analyses on the collected data and explored the influence of clinical, methodological, and operational trial characteristics on the reported adoption of data monitoring committees. RESULTS: We identified 13,928 pediatric randomized controlled trial records, of which 39.7% reported adopting a data monitoring committee, 49.0% reported not adopting a data monitoring committee, and 11.3% did not answer on this item. While the number of registered pediatric trials has been increasing since 2008, we found no clear time trend in the reported adoption of data monitoring committees. Data monitoring committees were more common in multicenter trials (50.6% vs 36.9% for single-center), multinational trials (60.2% vs 38.7% for single-country), National Institutes of Health-funded (60.3% vs 40.1% for industry-funded or 37.5% for other funders), and placebo-controlled (47.6% vs 37.5% for other types of control groups). Data monitoring committees were also more common among trials enrolling younger participants, trials employing blinding techniques, and larger trials. Data monitoring committees were more common in trials with at least one serious adverse event (52.6% vs 38.4% for those without) as well as for trials with reported deaths (70.3% vs 38.9% for trials without reported deaths). In all, 4.9% were listed as halted prematurely, most often due to low accrual rates. Trials with a data monitoring committee were more often halted for reasons related to scientific data than trials without a data monitoring committee (15.7% vs 7.3%). CONCLUSION: According to registry records, the use of data monitoring committees in pediatric randomized controlled trials was more frequent than previously reported in reviews of published trial reports. The use of data monitoring committees varied across key clinical and trial characteristics based on which their use is recommended. Data monitoring committees may still be underutilized in pediatric trials, and reporting of this item could be improved.

The preventive effect of PNF stretching exercise on sports injuries in physical education based on IoT data monitoring.

In physical education, in order to prevent sports injuries of students, it is necessary to study and build a set of physical education Internet of Things data monitoring training system to prevent sports injuries of students. This system is mainly composed of sensors, smart phones and cloud servers. Wearable devices equipped with sensors are used to complete data acquisition and transmission by means of the Internet of Things system, and relevant parameters are sorted and monitored by combining data analysis technology. The system makes a more in-depth, comprehensive and accurate analysis and processing of the collected data, so as to better evaluate the status and quality of students' sports, find out the existing problems in time, and put forward the corresponding solutions. By analyzing students' sports data and health data, the system generates personalized training programs, including training intensity, training time, training frequency and other parameters, so as to meet the needs and actual conditions of different students and avoid sports injuries caused by overtraining. This system can better analyze and process the collected data, provide teachers with more comprehensive and in-depth assessment and monitoring of students' sports status, and provide students with more personalized and scientific training programs, so as to effectively prevent the occurrence of students' sports injuries.