Parkinson's disease is a common neurodegenerative disorder touching 1.5% of the general population over 60 year-old and featuring impaired mobility with high impact on daily living and quality of life of the patients and their caregivers. Fourty percent of the patients with Parkinson's disease (PD) report inconstant, prominent, spontaneous, transitory improvement in mobility occurring on morning awakening, before taking their first morning dose of dopaminergic medications. This apparently unpredictable, highly variable, sleep-related phenomenon has been named "Sleep Benefit" (SB) by the scientists.
SB is a promising track to follow to develop novel therapeutic strategies for motor symptoms in PD. An innovative approach could be to induce modifications of mobility by influencing sleep regulation in PD patients in experimental settings.
Sleep propensity and timing depend on the coordinated interaction of the duration of preceding wakefulness (homeostatic component) and on a circadian signal (circadian component). Reciprocal interactions between homeostatic and circadian processes preside to internal synchrony of many physiological processes. We hypothesize SB to depend on serendipitous optimal synchronization between circadian and homeostatic process on morning awakening. As SB shows high day-to-day, inter- and intra-subject variability, studying SB requires multiple, repeated assessment of mobility during several days. A home-based experimental setting would be optimal for this purpose in terms of cost-effectiveness and acceptability by the patients. Moreover, considering that the range and nature of SB has not been well characterized so far, and that the amplitude of its variability is unknown, a reliable, observer- and situation-independent, reproducible assessment method of SB is a pivotal requirement for further research in this area.
A recently developed technique associating machine-learning algorithms with wireless wearable sensors (accelerometers and gyroscopes) and software applications might be particularly promising to characterize the complexity and multiplicity of SB in PD. Thanks to this technique, repeated, multiple assessments of mobility can be performed at patients' home without the constant presence of an investigator.
The working hypothesis of this study is that motor performance in PD patients improves on morning awakening when optimal synchrony between circadian and homeostatic regulation of sleep occurs. As first step, we envision to set up a home-based and technology-assisted methodology and to verify its scientific, technological and logistic feasibility.
The study will involve four work packages, for each of which specific endpoints are defined:
WP1: Definition of the logistics, setting, practices of the study procedures for home assessment;
WP2: Technological setup of:
* IMU wearable sensors
* SleepFit software application development
* light therapy (included sham light therapy)
* home polysomnography
* chronobiological assessments (distal-proximal skin body temperature gradient; Dim Light Melatonin Onset (DLMO) from salivary specimens;
Two work packages (3 and 4) will require patients inclusion and interventions on patients:
WP3: Validation of mobility assessment by wearable sensors: accuracy of machine learning algorithm to predict patients' motor status based on the MDS-UPDRS-III total score and on the 3.14 item (global clinical impression of mobility);
WP4: Testing in real-life conditions at patients' home in a small group of subjects.
