New Evidence that mHealth Technology Will Provide Better Data for Parkinson's Patients
(Boston, MA) – Parkinson’s disease (PD) is a slowly progressing neurodegenerative disorder, and the second most common neurodegenerative disorder behind Alzheimer’s disease. Each year more than 60,000 Americans are diagnosed with PD and the condition impacts more than 10 million people worldwide. While many researchers are looking at ways to improve Parkinson’s care, one major hurdle is collecting accurate reporting by research participants, which traditionally have relied on self-reporting diaries as a primary tool. A new article from a large industry and academia collaboration including Harvard Medical School, Spaulding Rehabilitation Network, Boston University School of Medicine, Pfizer inc., Tufts Medical Center and the Wyss Institute for Biologically Inspired Engineering at Harvard University has been published in Nature Partners Journal (npj) Digital Medicine titled "mHealth and Wearable Technology Should Replace Motor Diaries to Track Motor Fluctuations in Parkinson’s Disease” that examines using novel technologies to solve this problem.
“For clinicians to effectively manage the disease and for researchers to develop new therapies, high quality data are critical. By using technology to help us get accurate data and more of it, we feel this approach can be used to help us improve the care and research options for the population with Parkinson’s disease,” said Paolo Bonato, PhD. Dr. Bonato is the Director of the Spaulding Motion Analysis Lab, a Wyss Institute Associate Faculty member, a senior author on the paper and one of the principal investigators of the initiative.
Currently, questionnaires and motor diaries remain the primary tools for identifying and monitoring fluctuations in motor and non-motor symptoms. Concerns about the accuracy and reliability of motor diaries are driven by a number of factors including: the risk of fatigue that may lead to poor adherence by participants, the effects of recall bias, the limited time resolution they afford, and the nature of the data which measures only the duration of time spent in an identified state and not the severity of impairment (or magnitude of improvement) experienced by individuals with PD in response to treatment.
Digital measurement tools including mobile and wearable technologies are widely recognized for their potential to improve the resolution and efficacy of remote monitoring of people with PD. Accurately monitoring motor and non-motor symptoms, as well as complications resulting from symptoms and treatment, in people with PD is a major challenge both during clinical management and in the conduct of clinical trials investigating new treatments. “Digital technologies have the potential to enhance existing standard methods of assessing and monitoring Parkinson’s disease; not only [the patient’s] motor and non-motor symptoms in response to treatment, but also their functional ability to live an independent and fulfilling life,” stated Dr. Kip Thomas, the principal investigator of the Boston University arm of the collaboration. “Integrating digital measures into clinical trials will improve the consistency and resolution of the data to the benefit of the scientist, the clinician, and, most importantly, the patient.”
This collaborative endeavor was organized with the specific goal of investigating the potential use of mobile and wearable technologies in clinical trials of new pharmacotherapies targeting PD symptoms. To accomplish this, four non-interventional clinical studies were conducted over a period of three years, each organized by one of the collaborating institutions in coordination with each other. In total, 60 healthy volunteers (aged 23-69; 33 females) and 95 people with PD (aged 42-80; 37 females; years since diagnosis 1-24 years; Hoehn & Yahr 1-3) participated and were monitored in either a laboratory environment, a simulated apartment, or at home and in the community. In this first of a planned series of articles, the group focused on the utility and reliability of self-reports for describing motor fluctuations, the agreement between participants and clinical raters on the presence of motor complications, the ability of video raters to accurately assess motor symptoms, and the dynamics of tremor, dyskinesia, and bradykinesia in response to medication. Future studies publications will explore additional methods for estimating symptom severity based on multimodal sensor data collected in this effort.
“We certainly feel that this work shows the potential for further study with an expanded group that will hopefully provide new tools to treat Parkinson’s and support this population,” concluded Dr. Bonato.