Real-world Data in Parkinson’s Disease

Real-world Data in Parkinson’s Disease Explained

Real-world evidence is essential in medical research. Real-world data has numerous benefits over randomized controlled trials, particularly in studying Parkinson’s disease. In fact, Parkinson’s is one of the most prevalent neurodegenerative diseases worldwide, with no current treatments being able to cure the disease. Only in the US, there are more than 630,000 people affected by this condition (Tanguy, Jonsson & Ishihara, 2017).

Since managing Parkinson’s disease can be challenging, with symptoms fluctuating daily and between patients, collecting health-related data in real time is vital. Note that real-world data is defined as medical information collected in non-experimental environments. Sources vary from administrative claims to social media channels. Data across real-life settings and diverse populations can provide valuable insights into disease progression, natural history of the disease, treatment programs, and socioeconomic burdens. It can reveal important aspects of the daily lives of people with Parkinson’s disease and the effectiveness of novel treatments. Consequently, longitudinal real-world data can improve interoperability, Parkinson’s disease management, and financial decisions.

Real-world Data and Areas of Assessment in Parkinson’s Disease

Research in the field of oncology is sensitive and challenging. Note that there are various cancer treatments, with radiation therapy and chemotherapy being among the most common interventions (“Types of Cancer Treatments,” 2017). With the increasing use of digital health solutions and experimental cancer therapies, though, real-world data can support numerous aspects of medical research and routine clinical practice. Some of its applications include:

Parkinson’s disease is a complicated medical condition, defined as a central nervous system disorder. The wide range of symptoms and complications requires the use of different measurements for the effective management of the disease. Interestingly, a recent literature review explored the benefits of various Parkinson’s disease measurements, classified into the following dimensions of assessment (Tanguy, Jonsson & Ishihara, 2017).

  • Motor and neurological function: Parkinson’s disease, as explained earlier, is a complex disorder of the central nervous system. Common symptoms include shaking, difficulty with walking, and rigidity (Opara et al., 2017). Patients are also prone to falls. Comparing motor tests is one of the most significant indicators employed in Parkinson’s research. There are various motor assessments (e.g. of hand function), such as the Hoehn and Yahr stages of progression of the disease, the Unified Parkinson’s Disease Rating Scale, and the Timed Up and Go Test. Note that the latest advancements in medical technology allow the three-dimensional analysis of gait and movement in participants. Since motor symptoms affect patients’ daily living and quality of life, real-world data is needed to improve Parkinson’s disease assessment and treatment options. Note that preventing falls is one of the leading goals in routine clinical practice.
  • Cognition: Cognitive changes also occur in patients with Parkinson’s disease, especially in the late stages of the disease (Romann et al., 2012). Although there are various medications and existing treatments, delaying Parkinson’s symptoms is still a challenge in medical research. As mentioned earlier, there’s no cure for Parkinson’s; each patient receives treatment based on their individual symptoms. Note that in some cases, Parkinson’s treatments (e.g., deep brain stimulation) may worsen a patient’s cognitive functioning. Some of the common tools used to assess dementia in Parkinson’s patients include the Clock Drawing Test, Stroop Test, Digit Span, Trail Making Test, Wisconsin Card Sorting Test, and Verbal Fluency Test. In addition, real-world evidence can provide a comprehensive picture of one’s cognitive abilities and changes.
  • Psychiatric symptoms: Psychiatric symptoms can also be observed in Parkinson’s disease. Neuropathological findings indicate that brain dysfunction, medications, and the impact on daily living can lead to psychotic episodes, shame, and depression. Common scales to assess this dimension in patients with Parkinson’s include the Beck Depression Inventory, Parkinson Psychosis Questionnaire, Clinician Global Impression of Change, and Geriatric Depression Scale. Interestingly, when it comes to depression, statistics show that 40% of Parkinson’s patients experience depression and distress. Real-life data can benefit Parkinson’s research, including patient health outcomes.
  • Activities of daily living: Parkinson’s disease has a negative impact on daily living, especially in the late stages of the disease (Lee et al., 2016). Basic activities, such as feeding, bathing, and dressing, can be severely affected. Hence, the caregiver’s role is vital. Since Parkinson’s is a progressive neurological condition, real-world data is essential for the assessment of disease management and treatment. The Unified Parkinson’s Disease Rating Scale and self-reported outcomes are among the most popular tools used to study Parkinson’s disease and its impact on daily living.
  • Sleep quality: Sleep quality in patients with Parkinson’s disease declines over time (“Sleep disorders in Parkinson’s disease: Diagnosis and management,” 2011). Note that the Epworth Sleepiness Scale Sleep, the Apnea Scale of Sleep Disorders Questionnaire, and the Parkinson’s disease sleep scale can be utilized to assess this domain in patients with Parkinson’s disease. In addition, polysomnography can provide valuable clinical insights into a patient’s sleep patterns and symptoms. Interestingly, practice shows that the bed partner can also be affected (e.g., due to limb movements that prevent them from sleeping). Thus, longitudinal real-world data is needed to benefit patients and families.
  • Treatment: Although various treatments and medications (e.g., dopamine agonists) exist, Parkinson’s disease still challenges researchers and practitioners. Note that Levodopa is among the most effective medications to treat Parkinson’s. Research, however, shows that some medications can cause severe side effects. Therefore, Parkinson’s disease rehabilitation (including speech therapy, physiotherapy, and education) is becoming a leading approach in research. Real-world data, such as administrative claims, can provide beneficial information about treatment effectiveness, adherence, and long-term effects.
  • Quality of life: Quality of life is one of the main areas of digital health research. Real-world data can be utilized to improve patients’ quality of life. Since Parkinson’s disease leads to numerous symptoms, such as shaking, depression, and dementia, patients report low levels of quality of life (Opara et al., 2012). We should note that the quality of life is a complex construct, which includes physical, mental, and social aspects. It’s not surprising that the quality of life is becoming one of the leading factors in digital health research. Satisfaction with treatment, self-image, and social support also influence a patient’s well-being. Subjective measures are usually employed to measure the quality of life in patients with Parkinson’s disease. Assessments such as the Quality of Life in Neurological Disorders, 39-item Parkinson’s Disease Quality of Life, and 36-item Short Form can be utilized to measure a patient’s well-being and treatment effectiveness.
  • Autonomic symptoms: Research shows that autonomic symptoms in Parkinson’s disease include blood pressure, constipation, swallowing, sweating, and sexual dysfunction. Note that autonomic symptoms can affect a patient’s quality of life (Merola et al., 2018). Merola and colleagues revealed that autonomic symptoms could worsen by 20% over the course of 12 months. Therefore, scales for outcomes of Parkinson’s – Autonomic Symptoms should be employed to help researchers monitor patients. Sensors and wearable devices also provide valuable real-world evidence across a wide range of data points.
  • Other: Research of Parkinson’s disease has identified another crucial dimension, defined as Other (Tanguy, Jonsson & Ishihara, 2017). This broader research area includes aspects, such as olfaction, emotional support, and caregivers’ burden. Some beneficial measurements include the 16-item sniffin’ Sticks Odor Identification test, the Social Readjustment Rating Scale, and the Multidimensional Caregiver Strain Index, respectively. Longitudinal real-world data is needed to explore all these medical and socioeconomic problems in the long-term. Data can benefit patients as well as caregivers.

Yet, this research classification should be further harmonized across local and international research and administrative bodies.

Benefits of Real-world Data in Parkinson’s Disease and Digital Health

Parkinson’s is a progressive neurogenerative disease with severe complications. Since symptoms fluctuate on a daily basis and between patients, real-world data is needed to measure symptoms, assess treatments, benefit financial decisions, and improve patients’ quality of life. Note that a recent study showed that smartphone assessments could provide valuable clinical insights, including frequent real-world data (Zhan et al., 2018). Zhan and colleagues assessed 129 Parkinson’s patients who completed five tasks on a mobile app. The tasks measured finger tapping, balance, gait, voice, and reaction time, providing data from 6,148 smartphone activity assessments in total. mHealth apps can help patients with a chronic condition track their symptoms, manage medications, store insurance information, and find social support. Practice shows that mobile sensors which track symptoms and Parkinson’s apps which show exercises (e.g., to improve balance and posture) are highly beneficialwell-being. What’s more, Parkinson’s disease research shows that real-world measurements and remote monitoring can reduce research costs and improve health outcomes. Data collection in real time complements scientific findings: it provides vital information that annual medical visits are unable to capture. Last but not least, the mHealth approach in medicine is growing in popularity as it empowers and motivates patients. A recent initiative revealed that patients with Parkinson’s disease are willing to share their health-related data to contribute to research and routine clinical practice. Interestingly, 4,218 people from more than 50 countries provided a large source of medical information, including symptoms scores, patient-reported outcomes surveys, diary entries, and data from patients’ wearables.

With the transfer of medical information into electronic datasets, electronic health records and administrative claims also reveal some impressive benefits over standard clinical trials. In fact, although randomized controlled trials are still the gold standard in research, real-world data is vital. It can address some of the major obstacles in Parkinson’s disease research, such as strict inclusion criteria, poor generalizability, underrepresented populations, high costs, unexpected delays, and a lack of follow-up information (especially in non-pharmacological interventions). Medical claims databases, on the other hand, offer some impressive advantages over standard trials (Bloem et al., 2018). Administrative claims provide information about diverse populations, real-world settings, and co-morbid conditions. Investigating a medical claims dataset is more cost-effective than traditional clinical trials. Electronic health records also provide robust real-world data, including medical history and prescriptions, which can complement scientific findings.

Real-world Data in Parkinson’s Disease: Conclusion

In conclusion, real-world data plays an important role in research and practice. When it comes to Parkinson’s disease research, longitudinal real-world data benefit the evaluation of epidemiology, treatment management, and payment decisions. Parkinson’s disease, as explained above, is one of the most challenging neurological diseases, which affects millions of people worldwide. Since researchers and practitioners cannot capture all the varying symptoms and side effects associated with the disease, interventions are often individualized. Hence, longitudinal real-world data is essential to complement medical findings and improve clinical decisions.

With the increasing capabilities of today’s digital health solutions, electronic sources of real-world data are becoming more and more popular. Sources, such as administrative claims and mHealth apps, support data collection, and analysis across various domains of research (e.g., cognition, quality of life, etc.). Note that digital solutions facilitate data collection, increase user engagement, and empower patients. In the end, real-world evidence is reshaping the future of Parkinson’s research – improving patients health outcomes and quality of life.

References

Bloem, B., Ypinga, J., Willis, A., Canning, C., Barker, R., Munneke, M., & De Vriesa, N. (2018). Using Medical Claims Analyses to Understand Interventions for Parkinson Patients. Journal of Parkinson’s Disease, 8(1), p.45-58.

Lee, S., Kim, S., Cheon, S., Seo, J., Kim, M., & Kim, J. (2016). Activities of daily living questionnaire from patients’ perspectives in Parkinson’s disease: a cross-sectional study. BMC Neurology.

Merola, A., Romagnolo, A., Rosso, M., Suri, R., Berndt, Z., Maule, S., Lopiano, L., & Espay, A. (2018). Autonomic dysfunction in Parkinson’s disease: A prospective cohort study. Movement Disorders, 33(3), p. 391-397

Opara, J., Brola, W., Leonardi, M., & Blaszczyk, B. (2012). Quality of life in Parkinson`s Disease. Journal of Medicine and Life, 5(4), p. 375-381.

Opara, J., Malecki, A., Malecka, E., & Socha, T. (2017). Motor assessment in Parkinson`s disease. Annals of Agricultural and Environmental Medicine, 24(3), p. 411-415.

Romann, A., Dornelles, S., Maineri, N., Rieder, C., & Olchik, M. (2012). Cognitive assessment instruments in Parkinson’s disease patients undergoing deep brain stimulation. Dementia & Neuropsychologia.

Sleep disorders in Parkinson’s disease: Diagnosis and management (2011). Annals of Indian Academy of Neurology.

Tanguy, A., Jonsson, L., & Ishihara, L. (2017). Inventory of real-world data sources in Parkinson’s disease. BMC Neurology.

Zhan A, Mohan S, Tarolli C, Schneider R, Adams J, Sharma S, Elson M, Spear K, Glidden A, Little M, Terzis A, Dorsey E, Saria S. (2018). Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity The Mobile Parkinson Disease Score. JAMA Neurol, 75(7), p.876-880.

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Qolty Digital Health Research Tools with subjective, objective, advanced, and planning tools for your health research study with open pricing.

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Qolty Digital Health Research Tools with subjective, objective, advanced, and planning tools for your health research study with open pricing.

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