Sports participation and physical disabilities:Taking the hurdle?!

Slechts een op de drie mensen met een lichamelijke beperking sport regelmatig, terwijl tweederde van de mensen zonder lichamelijke beperking actief is in sport. Dit verschil is spijtig omdat sportdeelname juist ook voor mensen met een lichamelijke beperking vele gezondheidsvoordelen heeft, zoals een verminderde kans op hart- en vaatziekten en overgewicht. Om sportdeelname van mensen met een lichamelijke beperking te stimuleren, is het belangrijk te achterhalen waarom zij niet sporten, om vervolgens gerichte adviezen en maatregelen te nemen. Uit door ons uitgevoerde onderzoeken bij verschillende groepen mensen met een lichamelijke beperking, zowel sportende als niet sportende mensen, blijkt dat stimulansen vooral plezier en gezondheid zijn, naast steun vanuit de omgeving en sociale contacten. Belemmeringen om te sporten zijn vaak gerelateerd aan de onderliggende beperking of zijn van logistieke aard (sportfaciliteiten, transport, etc). Om mensen met een lichamelijke beperking meer te laten sporten moeten ze inzien en bij voorkeur ook ervaren hoe goed en leuk sporten kan zijn. Sport is een onderdeel van de revalidatiebehandeling en het revalidatieteam helpt mensen door informatie te geven over verschillende sportmogelijkheden en helpt bij het zoeken naar een geschikte sport. Ook de politiek zal haar verantwoordelijkheid voor het verbeteren van de gehandicaptensport moeten nemen door betere sportcondities te creëren, zoals het verbeteren van de sportfaciliteiten en transport en vergoedingen voor sporthulpmiddelen. Gemeentes zouden sportclubs moeten stimuleren om mensen met en zonder lichamelijke beperking samen te laten sporten. Want sport is er voor iedereen

Health Related Quality of Life in a Dutch Rehabilitation Population:Reference Values and the Effect of Physical Activity

Purpose To establish reference values for Health Related Quality of Life (HRQoL) in a Dutch rehabilitation population, and to study effects of patient characteristics, diagnosis and physical activity on HRQoL in this population. Method Former rehabilitation patients (3169) were asked to fill in a questionnaire including the Dutch version of the RAND-36. Differences between our rehabilitation patients and Dutch reference values were analyzed (t-tests). Effects of patient characteristics, diagnosis and movement intensity on scores on the subscales of the RAND-36 were analyzed using block wise multiple regression analyses. Results In total 1223 patients (39%) returned the questionnaire. HRQoL was significantly poorer in the rehabilitation patients compared to Dutch reference values on all subscales (p Conclusions HRQoL is poorer in rehabilitation patients compared to Dutch reference values. Physical components of HRQoL are affected by diagnosis. In rehabilitation patients an associatio

Effects of exergaming on exercise capacity in patients with heart failure: results of an international multicentre randomized controlled trial

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Data harmonization and federated learning for multi-cohort dementia research using the OMOP common data model:A Netherlands consortium of dementia cohorts case study

Background: Establishing collaborations between cohort studies has been fundamental for progress in health research. However, such collaborations are hampered by heterogeneous data representations across cohorts and legal constraints to data sharing. The first arises from a lack of consensus in standards of data collection and representation across cohort studies and is usually tackled by applying data harmonization processes. The second is increasingly important due to raised awareness for privacy protection and stricter regulations, such as the GDPR. Federated learning has emerged as a privacy-preserving alternative to transferring data between institutions through analyzing data in a decentralized manner. Methods: In this study, we set up a federated learning infrastructure for a consortium of nine Dutch cohorts with appropriate data available to the etiology of dementia, including an extract, transform, and load (ETL) pipeline for data harmonization. Additionally, we assessed the challenges of transforming and standardizing cohort data using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) and evaluated our tool in one of the cohorts employing federated algorithms. Results: We successfully applied our ETL tool and observed a complete coverage of the cohorts’ data by the OMOP CDM. The OMOP CDM facilitated the data representation and standardization, but we identified limitations for cohort-specific data fields and in the scope of the vocabularies available. Specific challenges arise in a multi-cohort federated collaboration due to technical constraints in local environments, data heterogeneity, and lack of direct access to the data. Conclusion: In this article, we describe the solutions to these challenges and limitations encountered in our study. Our study shows the potential of federated learning as a privacy-preserving solution for multi-cohort studies that enhance reproducibility and reuse of both data and analyses.</p

Data harmonization and federated learning for multi-cohort dementia research using the OMOP common data model:A Netherlands consortium of dementia cohorts case study

Background: Establishing collaborations between cohort studies has been fundamental for progress in health research. However, such collaborations are hampered by heterogeneous data representations across cohorts and legal constraints to data sharing. The first arises from a lack of consensus in standards of data collection and representation across cohort studies and is usually tackled by applying data harmonization processes. The second is increasingly important due to raised awareness for privacy protection and stricter regulations, such as the GDPR. Federated learning has emerged as a privacy-preserving alternative to transferring data between institutions through analyzing data in a decentralized manner. Methods: In this study, we set up a federated learning infrastructure for a consortium of nine Dutch cohorts with appropriate data available to the etiology of dementia, including an extract, transform, and load (ETL) pipeline for data harmonization. Additionally, we assessed the challenges of transforming and standardizing cohort data using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) and evaluated our tool in one of the cohorts employing federated algorithms. Results: We successfully applied our ETL tool and observed a complete coverage of the cohorts’ data by the OMOP CDM. The OMOP CDM facilitated the data representation and standardization, but we identified limitations for cohort-specific data fields and in the scope of the vocabularies available. Specific challenges arise in a multi-cohort federated collaboration due to technical constraints in local environments, data heterogeneity, and lack of direct access to the data. Conclusion: In this article, we describe the solutions to these challenges and limitations encountered in our study. Our study shows the potential of federated learning as a privacy-preserving solution for multi-cohort studies that enhance reproducibility and reuse of both data and analyses.</p

Data harmonization and federated learning for multi-cohort dementia research using the OMOP common data model: A Netherlands consortium of dementia cohorts case study

Background: Establishing collaborations between cohort studies has been fundamental for progress in health research. However, such collaborations are hampered by heterogeneous data representations across cohorts and legal constraints to data sharing. The first arises from a lack of consensus in standards of data collection and representation across cohort studies and is usually tackled by applying data harmonization processes. The second is increasingly important due to raised awareness for privacy protection and stricter regulations, such as the GDPR. Federated learning has emerged as a privacy-preserving alternative to transferring data between institutions through analyzing data in a decentralized manner. Methods: In this study, we set up a federated learning infrastructure for a consortium of nine Dutch cohorts with appropriate data available to the etiology of dementia, including an extract, transform, and load (ETL) pipeline for data harmonization. Additionally, we assessed the challenges of transforming and standardizing cohort data using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) and evaluated our tool in one of the cohorts employing federated algorithms. Results: We successfully applied our ETL tool and observed a complete coverage of the cohorts’ data by the OMOP CDM. The OMOP CDM facilitated the data representation and standardization, but we identified limitations for cohort-specific data fields and in the scope of the vocabularies available. Specific challenges arise in a multi-cohort federated collaboration due to technical constraints in local environments, data heterogeneity, and lack of direct access to the data. Conclusion: In this article, we describe the solutions to these challenges and limitations encountered in our study. Our study shows the potential of federated learning as a privacy-preserving solution for multi-cohort studies that enhance reproducibility and reuse of both data and analyses