Abstract
Accurate real-life monitoring of motor and non-motor symptoms is a challenge in Parkinson’s disease (PD). The unobtrusive capturing of symptoms and their naturalistic fluctuations within or between days can improve evaluation and titration of therapy. First-generation commercial PD motion sensors are promising to augment clinical decision-making in general neurological consultation, but concerns remain regarding their short-term validity, and long-term real-life usability. In addition, tools monitoring real-life subjective experiences of motor and non-motor symptoms are lacking. The dataset presented in this paper constitutes a combination of objective kinematic data and subjective experiential data, recorded parallel to each other in a naturalistic, long-term real-life setting. The objective data consists of accelerometer and gyroscope data, and the subjective data consists of data from ecological momentary assessments. Twenty PD patients were monitored without daily life restrictions for fourteen consecutive days. The two types of data can be used to address hypotheses on naturalistic motor and/or non-motor symptomatology in PD.
Highlights
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Complementary to continuous objective monitoring, continuous subjective monitoring via electronic (e-)diaries is suggested to contribute to both motor and non-motor Parkinson’s disease (PD) monitoring in real life [11,12]
We showed a good completion of objective and subjective data collection, with an acceptable burden for PD patients, and without high variability in completion between or within days
Summary
Parkinson’s disease (PD)’s world-wide prevalence is expected to double to over 12 million patients by 2040 [1]. We showed a good completion of objective and subjective data collection, with an acceptable burden for PD patients, and without high variability in completion between or within days These naturalistic long-term objective and subjective data aim to overcome the lack of unobtrusive, momentary, repetitive assessments of currently available PD motor monitoring devices [7,13,14]. Combining continuous objective and subjective data can help to overcome the well-known challenge of translating scripted, lab-based monitor methods, to unscripted, real-life monitor methods [19,20]. It can serve as an alternative, continuous gold standard informing about subjectively experienced PD symptomatology, parallel to naturalistic sensor data. The applied methodology to combine momentary objective data with high-frequency objective data can be extrapolated to (EMA-)research in general
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