Sleep benefit (SB) consists of a spontaneous, transient and inconsistent improvement of the mobility occurring on morning awakening in approximately 40% of Parkinson's disease (PD) patients, before taking the first morning dose of dopaminergic drugs. The SB could represent a pathway for the development of new therapeutic strategies for motor symptoms in PD. Being a seemingly unpredictable phenomenon and a great variability daily, inter- and intra-subject, the SB study requires multiple and repeated assessments of mobility for several days. An experimental home setting would be optimal for this purpose in terms of cost-effectiveness and patient acceptability. In addition, since the extent and nature of SB have not been well characterized so far, and the magnitude of its variability is unknown, a reliable assessment method, independent of observers and situation, the SB is a requirement of further research in this area. A recently developed technique combining machine learning algorithms with wireless portable sensors (accelerometers and gyroscopes) and software applications could be particularly promising for characterizing the complexity and multiplicity of SBs in. With this technique, repeated and multiple assessments of mobility can be performed in the homes of patients without the constant presence of a researcher. This approach offers several advantages in terms of cost-effectiveness, feasibility and acceptability of study protocols by patients. It also improves the ecological validity of subjective and objective estimates of mobility in these patients. The investigators chose to conduct this preliminary study on patients with PD rather than on healthy subjects, because SB is a phenomenon that has been described so far only in this population. Investigators also consider that the feasibility of the study will depend mainly on the patients' ability to move and the context of their own illness. SB is a phenomenon induced by sleep. The propensity and timing of sleep depend on the coordinated interaction of the duration of the previous awakening (homeostatic process) and a circadian signal (circadian process). In order to better understand SB, it is necessary to study the reciprocal influences of the circadian and homeostatic process. Investigators have devised a new paradigm to "shift" the circadian process phase around the homeostatic process, maintained under constant conditions, in order to observe the effect of the synchronism or desynchronization of these two processes on the awakening mobility of patients with an MP. This experimental approach was approved by Professor Aleksandar Videnovic (Harvard University School of Medicine, USA), opinion leader on circadian rhythmicity in the MP and scientific collaborator of this study. As a first step, the investigators plan to implement a technology-assisted home-based methodology, to validate it in PD patients and to verify the logistic feasibility of this method-assisted approach in a small group of patients, in order to to be able to apply this paradigm in larger scientific projects.
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.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
OTHER
Masking
NONE
The validity of the mobility assessment by IMU wearable sensors will be verified in Work Package 3. It will be defined as the accuracy of the machine learning algorithm to predict patients' motor status compared to the motor status assessed at clinical examination by means of the MDS-UPDRS-III scale and the Fit test. Prediction by machine learning will be compared with the MDS-UPDRS-III total score and with the 3.14 item (global clinical impression of mobility) of the same scale. The patients will be asked to perform all the motor tasks of the MDS-UPDRS-III scale and the finger tapping test (Fit test) with both hands wearing the IMU system. A subset of minimal motor tasks allowing good prediction of the patient's motor status by the machine learning algorithm will then be selected for Work Package 4.
Baseline Phase B1: Observation phase: 1-week wrist actigraphy and sleep diary to assess habitual activity/rest routines. B2: Nocturnal sleep consolidation phase: 2 weeks: timed light exposure, constant sleep/wake routine and sleep restriction of 1 hour/night (based on habitual activity).
Intervention phase * Phase A (11 days): subjects will receive one hour of morning and afternoon light exposure twice a day: * Day #1: baseline assessment of outcome measures; * Days #2 to #6: bright light in the morning (active treatment) + dim light in the afternoon (placebo), to progressively advance circadian phase of 30' per day (phase advance of 2.5 hours obtained at day 6); * Days #7 to #11: dim light in the morning + bright light in the afternoon to revert the circadian phase to the baseline of 30' per day. * Washout: 6 days. No intervention during this phase, to stabilise the reverted circadian rhythm to baseline, for phase B to be held at the same conditions as phase A. * Phase B (11 days): similar as in phase A: * Day #1: similar as in phase A; * Days #2 to #6: reverted conditions compared to phase A. * Days #7 to #11: reverted conditions compared to phase A.
CHU de Martinique
Fort-de-France, France
Validation of the objective metrics of mobility
The validity of the mobility assessment by Inertial Measurement Unit (IMU) wearable sensors will be verified. It will be defined as the accuracy of the machine learning algorithm to predict patients' motor status compared to the motor status assessed at clinical examination by means of the MDSUPDRS- III scale and the Fit test. Prediction by machine learning will be compared with the MDS-UPDRS-III total score and with the 3.14 item (global clinical impression of mobility) of the same scale. The patients will be asked to perform all the motor tasks of the MDS-UPDRS-III scale and the finger tapping test (Fit test) with both hands wearing the IMU system.
Time frame: 12 months
Objective and subjective mobility
Prediction of mobility by machine learning based on data from IMU wearable sensors; finger tapping test; VAS motor
Time frame: 12 months
Sleep and sleepiness
Measured by sleep diary, SSS
Time frame: 12 months
Cognition (electronic Stroop test)
Time frame: 12 months
Emotional state
Measured by Visual Analog Scale(VAS) mood/anxiety
Time frame: 12 months
Fatigue
Measured by VAS fatigue
Time frame: 12 months
Circadian phase
Continuously for skin body temperature and repeated samples (every 30' for a total of 9 samples, in the evening around the bed time, for salivary DLMO
Time frame: 12 months
Sleep homeostasis (SWA)
Calculated based on the EEG recording acquired by means of nocturnal portable polysomnography.
Time frame: 12 months
Chronotype
Measured by Horne \& Ostberg Morningness/Eveningness Questionnaire (MEQ)
Time frame: 12 months
Sleep habits, sleep and wake-related symptoms, sleep quality
Measured by Pittsburgh Sleep Quality Index (PSQI)
Time frame: 12 months
PD-specific sleep and wake-associated symptoms
Measured by Parkinson's Disease Sleep Scale (PDSS-2)
Time frame: 12 months
Daytime symptoms of bad or insufficient sleep
Measured by Epworth Sleepiness Scale (ESS) \[96\] and Fatigue Severity Scale (FSS)
Time frame: 12 months
Modification of mobility on morning awakening
Measured by Sleep benefit questionnaire
Time frame: 12 months
Motor and non-motor symptoms of PD in daily living
Measurded by MDS-UPDRS scale (parts I, II and IV)
Time frame: 12 months
Neuropsychological battery useful in idiopathic PD
Measured by Mattis dementia rating scale (MDRS)
Time frame: 12 months
Mood
Measured by Beck Depression Inventory (BDI)
Time frame: 12 months
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