Anonymous Data Research Articles

Observational Study of Men and Women with Breast Cancer in Terms of Overall Survival.

This cohort study represents a retrospective and anonymized data analysis of 2162 breast cancer cases (19 males and 2143 females), registered over a period of 12 years, from 1 January 2010 to 31 December 2021, in the Clinic of Obstetrics and Gynecology, Diakoneo Diak Klinikum Schwäbisch Hall, Germany. According to the Kaplan-Meier survival analysis, the estimated overall 3-year survival rate was 91.1% for women and 88.9% for men. The log-rank test of equality of survival distributions indicated a statistically significant difference in survival times between the two groups (p = 0.009). In the subsequent age-matched Kaplan-Meier analysis, the p-value was below the significance threshold (p = 0.068). Male breast cancer is a rare disease that may show some particularities in terms of survival compared to female breast cancer.

The Benchmarking Exercise Programme for Older People (BEPOP): Design, Results and Recommendations from The First Wave of Data Collection.

The Benchmarking Exercise Programme for Older People (BEPOP) service improvement project seeks to determine and promote the exercise training characteristics associated with positive outcomes for resistance exercise for older people living with, or at risk of, sarcopenia or physical frailty. Mixed-methods service improvement project. Ten UK National Health Service physiotherapist-led therapy services delivering exercise interventions for older people submitted anonymized data for up to 20 consecutive patients. A multidisciplinary expert panel generated a report and recommendations with site-specific benchmarking data and feedback. In parallel, participating physiotherapy team members were interviewed to elicit feedback on BEPOP rationale, processes and perceived value. Data from 188 patients were included, mean age 80 years (range 60-101). 115 (61%) received objective assessment of strength-based physical performance. Bodyweight exercises (173 [92%]) and resistance bands (49 [26%]) were the commonest exercise modalities. Exercises progressed predominantly through increased repetitions (163 [87%]) rather than increased load. 50 (30%) had no reassessment of outcomes; only 68 (41%) were signposted to follow-on exercise services. Staff interviews identified themes around knowledge, diagnosis, data collection and practice reflection. BEPOP was feasible to deliver and generated actionable insights for service improvement via improved diagnosis, measurement and progression of resistance exercise.

Automated data collection tool for real-world cohort studies of chronic hepatitis B: Leveraging OCR and NLP technologies for improved efficiency

BackgroundCollecting and standardizing clinical research data is a very tedious task. This study is to develop an intelligent data collection tool, named CHB-EDC, for real-world cohort studies of chronic hepatitis B (CHB), which can assist in standardized and efficient data collection. MethodsCHB_EDC is capable of automatically processing various formats of data, including raw data in image format, using internationally recognized data standards, OCR, and NLP models. It can automatically populate the data into eCRFs designed in the REDCap system, supporting the integration of patient data from electronic medical record systems through commonly used web application interfaces. This tool enables intelligent extraction and aggregation of data, as well as secure and anonymous data sharing. ResultsFor non-electronic data collection, the average accuracy of manual collection was 98.65 %, with an average time of 63.64 min to collect information for one patient. The average accuracy CHB-EDC was 98.66 %, with an average time of 3.57 min to collect information for one patient. In the same data collection task, CHB-EDC achieved a comparable average accuracy to manual collection. However, in terms of time, CHB-EDC significantly outperformed manual collection (p < 0.05). Our research has significantly reduced the required collection time and lowered the cost of data collection while ensuring accuracy. ConclusionThe tool has significantly improved the efficiency of data collection while ensuring accuracy, enabling standardized collection of real-world data.

Allocation Rules and Age-Dependent Waiting Times for Kidney Transplantation.

Rigid age limits in the current allocation system for post-mortem donor kidneys in Germany may have problematic effects. The new German national transplantion registry enables data analysis with respect to this question. Using anonymized data from the German national transplantion registry, we extracted and evaluated information on the recipients and postmortem donors of kidneys that were allocated in Germany through Eurotransplant over the period 2006-2020. Data on 19 664 kidney transplantations in Germany from 2006 to 2020 were analyzed. The median waiting time for kidney transplantation was 5.8 years. Persons under age 18 waited a median of 1.7 years; persons aged 18 to 64, 7.0 years; and persons aged 65 and older, 3.8 years. Over the period of observation, postmortem kidneys were transplanted into 401 people of age 64 (2.0% of all organ recipients) and 1,393 people of age 65 (7.1% of all organ recipients). The difference in waiting times between allocation programs for persons under age 65 (ETKAS, "Eurotransplant Kidney Allocation System") and those aged 65 and older (ESP, "Eurotransplant Senior Program") increased over the period of observation, from 2.6 years in 2006-2010 to 4.1 years in 2017-2020. The rigid age limits in the current allocation rules for post-mortem kidney donations in Germany are prolonging the waiting times for transplants among patients aged 18 to 64. We think these rules need to be fundamentally reassessed.

Investigating the impact of OCT imaging of the crystalline lens on the accuracy and precision of cataract assessment.

To determine if supplementing standard clinical assessments with Optical Coherence Tomography (OCT) imaging of the crystalline lens improves the accuracy and precision of lens opacity assessment and associated clinical management decisions by optometrists. Fifty optometrists registered in the UK or Éire undertook a clinical vignette study where participants graded lens opacities and made associated clinical management decisions based on the image(s)/information displayed. Three forms of vignettes were presented: (1) Slit-lamp (SL) images of the lens, (2) SL and OCT images and (3) SL, OCT and visual function measures. Vignettes were constructed using anonymised data from 50 patients with varying cataract severity, each vignette being presented twice in a randomised order (total vignette presentations = 300). The accuracy of opacity and management decisions were evaluated using descriptive statistics and non-parametric Bland-Altman analysis where assessments from experienced clinicians were the reference. The precision of assessments was examined for each vignette form using non-parametric Bland-Altman analysis. All (n = 50) participants completed the study, with 36 working in primary eyecare (primary eyecare) settings and 14 in hospital eyecare services (HES). Agreement was highest where vignettes contained all clinical data (i.e., SL, OCT and visual function data-grading: 51.0%, management: 50.5%), and systematically reduced with decreasing vignette content (p < 0.001). A larger number of vignettes containing imaging and visual function measures exhibited below reference (i.e., less conservative) grading compared with vignettes containing imaging data alone (all p < 0.05). HES-based optometrists were more likely to grade lens opacities lower than clinicians working in primary eyecare (p < 0.001). Good measurement precision was evident for all vignettes, with a mean bias close to zero and limits of agreement below one grading step for all conditions. The addition of anterior segment OCT to SL images improved the accuracy of lens opacity grading. Structural assessment alone yielded more conservative decision making, which reversed once visual functional data was available.

Privacy-Preserving Workflow for the Cross-Border Federated Analysis of Clinical Data.

The motivation behind this research is to perform a privacy-preserving analysis of data located at remote sites and in different jurisdictions with no possibility of sharing individual-level information. Here, we present key findings from requirements analysis and a resulting federated data analysis workflow built using open-source research software, where patient-level information is securely stored and never exposed during the analysis process. We present additional improvements to further strengthen the security of the workflow. We emphasize and showcase the use of data harmonization in the analysis. The data analysis is done using the R language for statistical computing and DataSHIELD libraries for non-disclosive analysis of sensitive data. The workflow was validated against two data analysis scenarios, confirming the results obtained with a centralized analysis approach. The clinical datasets are part of the large Pan-European SARS-Cov-2 cohort, collected and managed by the ORCHESTRA project. We demonstrate the viability of establishing a cross-border federated data analysis framework and conducting an analysis without exposing patient-level information, achieving results equivalent to centralized non-secure analysis. However, it is vital to ensure requirements associated with data harmonization, anonymization and IT infrastructure to maintain availability, usability and data security.

Supervisor’s neuroticism and problematic Internet use among graduate students: the mediating role of supervisor-student relationship quality and the moderating role of fear of the supervisor’s negative evaluation

BackgroundGraduate students exhibit vulnerability to problematic Internet use, which can result in adverse physical, psychological, and social consequences. However, limited studies have addressed this issue among graduate students, and even fewer have explored the unique factors contributing to their problematic Internet use. Therefore, to address this gap, the current study aims to probe the relationship between supervisor’s neuroticism and problematic Internet use among graduate students, the mediating effect of the supervisor-student relationship quality, as well as the moderating effect of fear of the supervisor’s negative evaluation.MethodsA cross-sectional study was conducted in 2018 at three universities in Beijing, China. Anonymous data from 448 graduate students were collected regarding problematic Internet use, supervisor’s neuroticism, supervisor-student relationship quality, and the fear of the supervisor’s negative evaluation. A moderated mediation analysis was performed using Hayes’ PROCESS macro (Model 14).ResultsSupervisor’s neuroticism was positively linked to graduate students’ problematic Internet use, supervisor-student relationship quality mediated the linkage, and fear of the supervisor’s negative evaluation played a moderating role in the second stage. Specifically, for students lower in fear of the supervisor’s negative evaluation, supervisor-student relationship quality negatively predicted students’ problematic Internet use. While for the graduate students higher in fear of the supervisor’s negative evaluation, supervisor-student relationship quality could not significantly predict students’ problematic Internet use. The mediating effect was only significant for graduate students lower in fear of the supervisor’s negative evaluation.ConclusionsThis study established a theoretical model linking supervisor’s neuroticism to graduate students’ problematic Internet use, highlighting the potential roles of supervisor-student relationship quality and fear of the supervisor’s negative evaluation. Reducing the neuroticism level of the supervisor, enhancing the quality of the supervisor-student relationship, and mitigating students’ fear of the supervisor’s negative evaluation will contribute to the reduction of problematic Internet use among graduate students.

Using Data Anonymization in big data analytics security and privacy

Big Data and Analytics mean an enormous and complex collection of very diverse information, which is processed with various technologies and methods to produce and deliver useful and valuable insights. Analytics is the science of using data, or information to extract useful and actionable insights, facts and knowledge from a collection of data it could be stated that Big Data Analytics is the best thing since every commercial data system ever built, although everybody with a more optimistic vision of technology would like to take note that there is a fine line where Everything Data crosses the boundary to something else, especially with regard to privacy and security of the world as we know it. Privacy and security are two distinct but closely related phenomena. Whereas privacy refers to the control over access to the individual, security refers to the stability or strength of controls designed to protect the individual’s privacy. There are many obvious considerations and obstacles when attempting to securely share data. During big data analytics, many invasive techniques such as data fusion, cross-correlation, and algorithm training are often conducted over shared data, which can lead to severe privacy leaks. This means that every enterprise, organization, and individual maintaining large data repositories are in danger of being breached. Our study teaches us that security, privacy, and ethical concerns in big data analytics do not exist in parallel to the business cycle, but must be wisely and ethically managed in coherence throughout all emerging processes of the big data and information systems.

Pharmacokinetic variability of everolimus and impact of concomitant antiseizure medications in patients with tuberous sclerosis complex: A retrospective study of therapeutic drug monitoring data in Denmark and Norway.

The mTOR-inhibitor everolimus is a precision drug with antiepileptogenic properties approved for treatment of epilepsy in persons with tuberous sclerosis complex (TSC) in combination with other antiseizure medications (ASMs). However, the pharmacokinetic variability of everolimus is scarcely described, and the available information on pharmacokinetic interactions is scarce. The purpose of this study was to investigate pharmacokinetic variability of everolimus in patients with TSC, and the impact of age, sex and comedication. In this retrospective observational study we used anonymized data from medical records of patients with TSC using everolimus in Norway and Denmark, 2012 to 2020. Long-term therapeutic drug monitoring (TDM) identified inter-patient and intra-patient variability. The study included 59 patients, (36 females (61%)), median age 22 (range 3-59 years). Polytherapy was used in 50 patients (85%). The most frequently used ASMs were lamotrigine (n = 21), valproate (n = 17), and levetiracetam (n = 13). Blood concentrations of everolimus were measured in all patients. Pharmacokinetic variability of everolimus between patients was extensive, as demonstrated by a 24-fold variability from minimum-maximum concentration/dose (C/D)-ratios. The coefficient of variation (CV) for intra-patient (n = 59) and inter-patient variability (n = 47, ≥3 measurements) was 40% and 43%, respectively. The C/D-ratio of everolimus was 50% lower in 13 patients (22%) using enzyme-inducing ASMs compared to the 30 patients who did not (0.7 vs 1.4 ng/mL mg, P < .05). Age and sex were not significantly associated with changes in C/D-ratios of everolimus. Long-term TDM identified extensive variability in concentrations over time for everolimus both within and between patients, where comedication with enzyme-inducing ASMs was an important contributing factor. The findings suggest a need for TDM in patients with TSC treated with everolimus.

Does COVID-19 Revamp Nurses’ Compassion? Post-Pandemic Approach in Qatar

Aim: This study aimed to identify self-compassion among staff nurses after the COVID-19 pandemic in Qatar. Design: Descriptive cross-sectional survey design. Methods: Anonymous data were collected through an online survey using Microsoft Forms from 300 nurses in 14 health facilities in Qatar. The organization had almost 10,000 nursing staff working in different facilities. Data were gathered using a structured online questionnaire and included socio-demographic information, and the Self-Compassion Scale—Short Form was used to collect the remaining data. Correlation, t-test, and ANOVA analyses were conducted. Results: Nurses in the study showed high self-compassion. Among the sub-domain ‘mindfulness’, they showed comparatively high scores (7.96 ± 1.55), and the lowest score was for ‘isolation’ (6.15 ± 1.99). The score for ‘self-kindness’ was 7.29 ± 1.55, that for ‘self-judgement’ was 6.79 ± 2.01, that for ‘common humility’ was 6.62 ± 1.47, and that for the sub-domain ‘over-identified’ was 6.47 ± 1.91. Mindfulness scores were high among the nurse leaders. Moreover, over-identified scores were high among the nurses who were currently working under COVID-19 at the time of data collection. Conclusions: Nurses faced many difficulties while working during the COVID-19 pandemic, including a heavy workload and tension. The current study’s findings add to our understanding of how COVID-19 affected the development of self-compassion. A rise in mindfulness, which aids nurses in managing stress at work and building resilience, further underscores an increase in nurses’ acceptance of the COVID-19 pandemic. The findings also highlight how crucial it is to encourage self-compassion in individuals and offer them emotional support at such times, especially when there is a significant risk factor for mental health, such as COVID-19.

Potential, Pitfalls, and Future Directions for Remote Monitoring of Chronic Respiratory Diseases: Multicenter Mixed Methods Study in Routine Cystic Fibrosis Care.

The current literature inadequately addresses the extent to which remote monitoring should be integrated into care models for chronic respiratory diseases (CRDs). This study examined a remote monitoring program (RMP) in cystic fibrosis (CF) by exploring experiences, future perspectives, and use behavior over 3 years, with the aim of developing future directions for remote monitoring in CRDs. This was a mixed methods, multicenter, observational study in 5 Dutch CF centers following a sequential explanatory design. Self-designed questionnaires using the technology acceptance model were sent out to people with CF who had a minimum of 12 months of experience with the RMP and local health care professionals (HCPs). Questionnaire outcomes were used to inform semistructured interviews with HCPs and people with CF. Qualitative findings were reported following the COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist. Anonymous data on use frequency of all people with CF were analyzed. Between the second quarter of 2020 and the end of 2022, a total of 608 people with CF were enrolled in the program, and a total of 9418 lung function tests and 2631 symptom surveys were conducted. In total, 65% (24/37) of HCPs and 89% (72/81) of people with CF responded to the questionnaire, and 7 HCPs and 12 people with CF participated in semistructured interviews. Both people with CF and HCPs were positive about remote monitoring in CF care and found the RMP a good addition to daily care (people with CF: 44/72, 61%; HCPs: 21/24, 88%). Benefits ranged from supporting individual patients to reducing health care consumption. The most valued monitoring tool was home spirometry by both people with CF (66/72, 92%) and HCPs (22/24, 92%). Downsides included the potential to lose sight of patients and negative psychosocial effects, as 17% (12/72) of people with CF experienced some form of stress due to the RMP. A large majority of people with CF (59/72, 82%) and HCPs (22/24, 92%) wanted to keep using the RMP in future, with 79% (19/24) of HCPs and 75% (54/72) of people with CF looking forward to more replacement of in-person care with digital care during periods of well-being. Future perspectives for the RMP were centered on creating hybrid care models, personalizing remote care, and balancing individual benefits with monitoring burden. Remote monitoring has considerable potential in supporting people with CF and HCPs within the CF care model. We identified 4 practice-based future directions for remote monitoring in CF and CRD care. The strategies, ranging from patient driven to prediction driven, can help clinicians, researchers, and policy makers navigate the rapidly changing digital health field, integrate remote monitoring into local care models, and align remote care with patient and clinician needs.

Blockchain-based anonymous authentication and data aggregation for advanced metering infrastructure in smart grid

This paper proposes a blockchain-based scheme, focusing on anonymous identity authentication and data aggregation, for safer and more reliable bidirectional communication between the utility company and power consumers based on Advanced Metering Infrastructure (AMI). Firstly, to cope with the mutual identity authentication between resource-constrained Smart Meters, a lightweight anonymous authentication strategy is designed using Elliptic Curve Cryptography. Meanwhile, a reputation-based consensus protocol is developed to accomplish data aggregation in AMI by using decentralization and non-tampering features of the blockchain. During the communication, in each time slot, the proposed scheme only needs to select a trusted user randomly to undertake the mining node rather than to introduce a third-party in a centralized manner to summarize and record the user-side data into the blockchain. Such scheme can effectively prevent data tampering and also be effective to decrease the processing complexity. The AVISPA tool is adopted to formally evaluate its security. The simulation results show promising performance. The proposed scheme not only can guarantee secure communication but also effectively decrease the computational cost in AMI.

The implementation specifics of the right to be forgotten in the context of the decisions of the Court of Justice of the European Union and the European Court of Human Rights

The article is dedicated to the legal analysis of the case law of the CJEU and the ECtHR regarding the implementation of the right to be forgotten and the right to privacy. It is established that the development of digital technologies has enabled rapid information exchange and its almost indefinite storage in the digital environment. However, such widespread availability of data about individuals and the inability to update or supplement this information have posed new challenges to society in ensuring the protection of personal data and privacy. The right to be forgotten is one of the tools through which a person can delete truthful but outdated information about themselves on the Internet, provided that such information is not of public interest. It is proven that although the right to be forgotten has been implemented within the EU for over a decade, lawyers and human rights activists consider it the second most complex obligation of the General Data Protection Regulation to implement. It is argued that both the CJEU and the ECtHR often inadequately describe the factual circumstances of cases and disregard the confidentiality of the parties involved. Individuals who apply to the court for the deletion or anonymization of personal data on the Internet often receive the opposite effect due to the public and scientific community’s interest in such cases. It is argued that in order to maintain a reasonable balance between privacy and freedom of expression, court decisions should contain all factual circumstances of the case, but the personal data of the parties involved and other circumstances that may indicate the applicant should be anonymized. This will help avoid censorship, ensure the right to memory, and enable an understanding of the factual basis on which courts have developed their legal arguments. It is argued that since the materials of cases before the ECtHR and the CJEU are often transmitted to national courts of member states for quality protection of confidentiality, it is necessary for them to correctly anonymize the personal data of the parties involved in the case and information that could identify the applicant.

Understanding the Use of Mobility Data in Disasters: Exploratory Qualitative Study of COVID-19 User Feedback.

Human mobility data have been used as a potential novel data source to guide policies and response planning during the COVID-19 global pandemic. The COVID-19 Mobility Data Network (CMDN) facilitated the use of human mobility data around the world. Both researchers and policy makers assumed that mobility data would provide insights to help policy makers and response planners. However, evidence that human mobility data were operationally useful and provided added value for public health response planners remains largely unknown. This exploratory study focuses on advancing the understanding of the use of human mobility data during the early phase of the COVID-19 pandemic. The study explored how researchers and practitioners around the world used these data in response planning and policy making, focusing on processing data and human factors enabling or hindering use of the data. Our project was based on phenomenology and used an inductive approach to thematic analysis. Transcripts were open-coded to create the codebook that was then applied by 2 team members who blind-coded all transcripts. Consensus coding was used for coding discrepancies. Interviews were conducted with 45 individuals during the early period of the COVID-19 pandemic. Although some teams used mobility data for response planning, few were able to describe their uses in policy making, and there were no standardized ways that teams used mobility data. Mobility data played a larger role in providing situational awareness for government partners, helping to understand where people were moving in relation to the spread of COVID-19 variants and reactions to stay-at-home orders. Interviewees who felt they were more successful using mobility data often cited an individual who was able to answer general questions about mobility data; provide interactive feedback on results; and enable a 2-way communication exchange about data, meaning, value, and potential use. Human mobility data were used as a novel data source in the COVID-19 pandemic by a network of academic researchers and practitioners using privacy-preserving and anonymized mobility data. This study reflects the processes in analyzing and communicating human mobility data, as well as how these data were used in response planning and how the data were intended for use in policy making. The study reveals several valuable use cases. Ultimately, the role of a data translator was crucial in understanding the complexities of this novel data source. With this role, teams were able to adapt workflows, visualizations, and reports to align with end users and decision makers while communicating this information meaningfully to address the goals of responders and policy makers.

An Anonymous and Secure Data Transmission Mechanism With Trajectory Tracking for D2D Relay Communication in 3GPP 5G Networks

An Anonymous and Secure Data Transmission Mechanism With Trajectory Tracking for D2D Relay Communication in 3GPP 5G Networks

Blockchain-based anonymous (IOT) data integrity auditing scheme in cloud environments

Blockchain-based anonymous (IOT) data integrity auditing scheme in cloud environments

A Data Ingestion Procedure towards a Medical Images Repository.

This article aims to create a repository of 33,000 medical CT images and 33,000 diagnostic reports with international standards (HL7 HAPI FHIR, DICOM, SNOMED). This goal requires devising a data ingestion procedure that can be replicated by other provider institutions, guaranteeing data privacy by implementing a pseudo-anonymization algorithm at the source, and generating labels from annotations via NLP. Our approach involves hybrid on-premise/cloud deployment of PACS and FHIR services, including transfer services for anonymized data to populate the repository through a structured ingestion procedure. We used NLP over the diagnostic reports to generate annotations, which were then used to train ML algorithms for content-based similar exam recovery. We successfully implemented ALPACS and PROXIMITY 2.0, ingesting almost 19,000 thorax CT exams to date along with their corresponding reports.

Exploring the Viability of ChatGPT for Personal Data Anonymization in Government: A Comprehensive Analysis of Possibilities, Risks, and Ethical Implications

Exploring the Viability of ChatGPT for Personal Data Anonymization in Government: A Comprehensive Analysis of Possibilities, Risks, and Ethical Implications

Big data anonymization using Spark for enhanced privacy protection

This article introduces an advanced solution for anonymizing large-scale sensitive data, addressing the limitations of traditional approaches when applied to vast datasets. By leveraging the Spark distributed computing framework, we propose a method that parallelizes the data anonymization process, enhancing efficiency and scalability. Utilizing Spark's resilient distributed datasets (RDD), the approach integrates two primary operations, Map_RDD and ReduceByKey_RDD, to execute the anonymization tasks. Our comprehensive experimental evaluation demonstrates our solution's effectiveness and improved performance in preserving data privacy while balancing data utility and confidentiality. A significant contribution of our study is the development of a wide array of solutions for data owners, particularly notable for a 500 MB dataset at an anonymity level of K=100, where our methodology produces 832 unique solutions. This study also opens avenues for future research in applying different privacy models within the Spark ecosystem, such as l-diversity and t-closeness.

The Spider: a visual, multisystemic symptom impact questionnaire for people with hypermobility-related disorders—validation in adults

IntroductionHypermobility spectrum disorders (HSD) and hypermobile Ehlers-Danlos syndrome (hEDS) are often accompanied by varied and complex multisystemic comorbid symptoms/conditions. The Spider questionnaire was developed to evaluate the presence and impact of eight common multisystemic comorbidities. Thirty-one questions across eight symptom domains assess neuromusculoskeletal, pain, fatigue, cardiac dysautonomia, urogenital, gastrointestinal, anxiety, and depression symptoms. This study aimed to evaluate the Spider’s construct validity in adults.MethodA cross-sectional observational study was conducted over four stages. Three international patient charities aided recruitment of participants through social media and website advertisements. Adults aged 18 to 65 years, with and without HSD/hEDS, were invited to participate. Validated, frequently used comparator questionnaires were used to establish convergent validity of Spider symptom domains. A control group was recruited for known-group validity analysis. Participants answered each Spider domain and the corresponding comparator questionnaire via surveys hosted by REDCap. Anonymous data were analysed using SPSS. Convergent validity was assessed through Spearman’s correlational analysis and known-group validity through Mann–Whitney U analysis.ResultsA total of 11,151 participants were recruited across the four stages. Statistically significant, moderate-to-strong correlations were found between all Spider domains and their comparators (p < 0.001, r = 0.63 to 0.80). Known-group validity analysis showed statistically significant differences (p < 0.001) between the hypermobile and control groups in all eight domains.ConclusionsConvergent and known-group validity of the Spider was established with adults. These results suggest the Spider can measure the presence and impact of multisystemic comorbid symptoms/conditions in adults with HSD/hEDS, providing a tool which guides multidisciplinary management.Key Points• The Spider questionnaire is a novel tool assessing the presence and impact of the multisystemic comorbid symptoms/conditions associated with HSD/hEDS.• Convergent and known-group validity of the Spider questionnaire was established in adults aged 18 to 65.• This tool provides a quick and easy method to visualise the symptom profile of those with HSD/hEDS to guide symptom management.