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Cited by 2 publications
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ParkProReakt - Evaluation of a proactive approach to health care in Parkinson’s disease: a study protocol for a randomised controlled trial
Introduction Parkinson's disease (PD) causes significant impairment due to both motor and non-motor symptoms, which severely impact patients' health-related quality of life (HRQoL) and increase caregiver burden. Given the rising prevalence of PD in an aging population, particularly in Germany, the need for innovative and resource-efficient healthcare approaches is paramount. The complexity of PD symptoms and the necessity for individualised, multidisciplinary and digital health technology-based care are widely acknowledged; however, access to specialist care remains limited, particularly in rural areas. Current healthcare systems are frequently ill-equipped to deliver timely, personalised interventions. In response to these challenges, the ParkProReakt project aims to enhance PD care through a proactive, technology-enabled, multidisciplinary approach designed to improve patient HRQoL and alleviate caregiver burden. Methods and analysis A randomised controlled trial will assess the efficacy and cost-effectiveness of ParkProReakt - a proactive, multidisciplinary, digitally supported care model for community-dwelling people with Parkinson's disease (PwPD) - compared with standard care. We will recruit a total of 292 PwPD and their informal caregivers living in two diverse regions in Germany. The primary outcome measure will be patients' HRQoL as measured by the PDQ-39, obtained at baseline, monthly and at completion of participation. Secondary outcomes comprise patients' subjective wellbeing, incidence or change of long-term care needs, global cognition and disease progression, utilisation of health care services including hospitalisations, caregiver burden and health care costs. Statistical analysis will include t-tests for HRQoL changes, GLM for confounders, and multilevel models for centre effects. Secondary outcomes and cost-effectiveness (ICER) will be analysed similarly, using R and SPSS. Ethics and dissemination The study protocol has been approved by the Ethics Committees of the Medical Associations of Hesse and Hamburg. The results of our study will be reported to the funding body and disseminated through scientific publications and presentations at national and international conferences. Registration details This study was registered with the German Registry for Clinical Studies (DRKS) in both German and English - number: DRKS00031092. What is already known on this topic Parkinson's disease imposes severe motor and non-motor challenges on patients, impacting their quality of life and caregiver well-being. The complexity of symptoms necessitates individualized, multidisciplinary, and digital health-based approaches to care. Despite a recognized need for proactive, scalable interventions in Parkinson's disease care, existing health systems have limited capacity for implementing these comprehensive, resource-efficient models effectively. What this study adds This study introduces a novel, proactive, technology-based, patient-centered model of care for people with Parkinson's disease, integrating wearable technology and an app to improve patient health-related quality of life. It rigorously assesses this model's effectiveness and cost-efficiency in Germany. How this study might affect research, practice or policy The study's findings could inform policy on proactive digital care for aging populations, improve Parkinson's disease care accessibility, and offer a framework for chronic disease management using patient-centered, cost-effective, and multidisciplinary approaches.
ParkProReakt - Evaluation of a proactive approach to health care in Parkinson’s disease: a study protocol for a randomised controlled trial
Introduction Parkinson's disease (PD) causes significant impairment due to both motor and non-motor symptoms, which severely impact patients' health-related quality of life (HRQoL) and increase caregiver burden. Given the rising prevalence of PD in an aging population, particularly in Germany, the need for innovative and resource-efficient healthcare approaches is paramount. The complexity of PD symptoms and the necessity for individualised, multidisciplinary and digital health technology-based care are widely acknowledged; however, access to specialist care remains limited, particularly in rural areas. Current healthcare systems are frequently ill-equipped to deliver timely, personalised interventions. In response to these challenges, the ParkProReakt project aims to enhance PD care through a proactive, technology-enabled, multidisciplinary approach designed to improve patient HRQoL and alleviate caregiver burden. Methods and analysis A randomised controlled trial will assess the efficacy and cost-effectiveness of ParkProReakt - a proactive, multidisciplinary, digitally supported care model for community-dwelling people with Parkinson's disease (PwPD) - compared with standard care. We will recruit a total of 292 PwPD and their informal caregivers living in two diverse regions in Germany. The primary outcome measure will be patients' HRQoL as measured by the PDQ-39, obtained at baseline, monthly and at completion of participation. Secondary outcomes comprise patients' subjective wellbeing, incidence or change of long-term care needs, global cognition and disease progression, utilisation of health care services including hospitalisations, caregiver burden and health care costs. Statistical analysis will include t-tests for HRQoL changes, GLM for confounders, and multilevel models for centre effects. Secondary outcomes and cost-effectiveness (ICER) will be analysed similarly, using R and SPSS. Ethics and dissemination The study protocol has been approved by the Ethics Committees of the Medical Associations of Hesse and Hamburg. The results of our study will be reported to the funding body and disseminated through scientific publications and presentations at national and international conferences. Registration details This study was registered with the German Registry for Clinical Studies (DRKS) in both German and English - number: DRKS00031092. What is already known on this topic Parkinson's disease imposes severe motor and non-motor challenges on patients, impacting their quality of life and caregiver well-being. The complexity of symptoms necessitates individualized, multidisciplinary, and digital health-based approaches to care. Despite a recognized need for proactive, scalable interventions in Parkinson's disease care, existing health systems have limited capacity for implementing these comprehensive, resource-efficient models effectively. What this study adds This study introduces a novel, proactive, technology-based, patient-centered model of care for people with Parkinson's disease, integrating wearable technology and an app to improve patient health-related quality of life. It rigorously assesses this model's effectiveness and cost-efficiency in Germany. How this study might affect research, practice or policy The study's findings could inform policy on proactive digital care for aging populations, improve Parkinson's disease care accessibility, and offer a framework for chronic disease management using patient-centered, cost-effective, and multidisciplinary approaches.
Compound Optimum Designs for Clinical Trials in Personalized Medicine
We consider optimal designs for clinical trials when response variance depends on treatment and covariates are included in the response model. These designs are generalizations of Neyman allocation, and commonly employed in personalized medicine where external covariates linearly affect the response. Very often, these designs aim at maximizing the amount of information gathered but fail to assure ethical requirements. We analyze compound optimal designs that maximize a criterion weighting the amount of information and the reward of allocating the patients to the most effective/least risky treatment. We develop a general representation for static (a priori) allocation and propose a semidefinite programming (SDP) formulation to support their numerical computation. This setup is extended assuming the variance and the parameters of the response of all treatments are unknown and an adaptive sequential optimal design scheme is implemented and used for demonstration. Purely information theoretic designs for the same allocation have been addressed elsewhere, and we use them to support the techniques applied to compound designs.
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