Real-PD Trial: Development of Clinical Prognostic Models for Parkinson's Disease

• Demographic and disease diagnostic questions as a measure of patients' profile [ Time Frame: Patients will be assessed once during the follow-up time. It is expected that the assessment will be performed in within up to 10 weeks after the enrollment date. ]

  A demographic questionnaire will be used. In that questionnaire the following questions must be answered:

  1. Age at the disease onset;
  2. Ethnicity;
  3. Level of education;
  4. Time since diagnoses. A descriptive analyses (e.g. mean, standard deviation and frequency) will be run in other to create the patients' profile.
  
  
• Parkinson's disease symptoms [ Time Frame: Patients will be assessed once during the follow-up time. It is expected that the assessment will be performed in within up to 10 weeks after the enrollment date. ]
  The Unified Parkinson's disease rating scale (MDS-UPDRS) will be used to collect data. Afterwards, descriptive analyses (e.g. mean and standard deviation) will be used to extract the disease status of the sample.
  
  
• Sleepiness rates in the Epworth sleepiness scale as a measure of sleep quantity [ Time Frame: Patients will be assessed once during the follow-up time. It is expected that the assessment will be performed in within up to 10 weeks after the enrollment date. ]
  The Epworth sleepiness scale will be used to rate the level of sleepiness during the day. The scale's scores are related to the usual duration of sleep at night and increase with relative sleep deprivation. Then, we can suggest that if the sample has high scores they will have a low sleep quantity.
  
  
• Depression scores as a measure of depression rates [ Time Frame: Patients will be assessed once during the follow-up time. It is expected that the assessment will be performed in within up to 10 weeks after the enrollment date. ]
  The scores obtained with the Geriatric Depression Scale will be analysed in order to create a rate of probably depression among the participants.
  
  
• Cognitive performance at the cognitive assessment [ Time Frame: Patients will be assessed once during the follow-up time. It is expected that the assessment will be performed in within up to 10 weeks after the enrollment date. ]
  The scores obtained with the Montreal Cognitive Assessment will be analyses in order to search for a possible cognitive impairment among the participants.
  
  
• Level of functionality in daily life based at the functionality test [ Time Frame: Patients will be assessed once during the follow-up time. It is expected that the assessment will be performed in within up to 10 weeks after the enrollment date. ]
  The scores for functionality obtained with the Schwab and England activities of daily living will be analyses in order to describe the functional level of the sample.
  
  
• Scores in autonomic dysfunctions measure with the autonomic dysfunctions scale [ Time Frame: Patients will be assessed once during the follow-up time. It is expected that the assessment will be performed in within up to 10 weeks after the enrollment date. ]
  The scores for autonomic dysfunctions will be obtained with theAssessment of autonomic dysfunction in Parkinson's disease (SCOPA-AUT). As high scores in the assessment are correlated with more presence of autonomic dysfunctions in Parkinson's patients, the presence of autonomic dysfunctions can be quantified.
  
  
• Number of medication intake annotations for each patient measured with the self-report app. [ Time Frame: Patients will be assessed during the follow-up time (up to 13 weeks after the enrollment date). It is expected that the assessment (self-report) will be performed every time that the patients take medication during the day or night. ]
  The number of medication intake annotations made by the patients will be collected through the smartphone application. Every time that the patient take medication they must press the button reporting that they took the medication. At the end of the follow-up time a sum of all the reports will be done in order to measure the number of medication intake over the follow-up time.
  
  
• Number of mood reports for each patient measured with a four point scale [ Time Frame: Patients will be assessed during the follow-up time (up to 13 weeks after the enrollment date). It is expected that the assessment (self-report) will be performed as many times as the patient wants to report how they feel or at least once a day. ]
  The number of mood reports will be collected through the smartphone application. A four point scale (very good, good, poor and fair) will be available, and by pressing the button which correspond to how the patient feels at that moment the report can be performed. At the end of the follow-up time a sum of all the reports will be done in order to measure the number of mood reports over the follow-up time.
  
  
• Time that each patient was active during the day [ Time Frame: Patients will be automatically assessed during the follow-up time (up to 13 weeks after the enrollment date), 24 hours a day, 7 days a week. ]
  The time that the patient was active during the day is calculated automatically through the app at the smartphone. The calculation is performed by using an algorithm, which analyze the patterns of walk. This algorithm is able to predict when the patient was active in a zone above his/her usual threshold (e.g. when the patient was performing one activity that makes him/her more active than during a quiet time). At the end of the follow-up time a sum of all active hours will be done in order to measure the amount of time that the patient was active over the follow-up time.
  
  
• Level of activity for each patient during the day [ Time Frame: Patients will be automatically assessed during the follow-up time (up to 13 weeks after the enrollment date), 24 hours a day, 7 days a week. ]
  The level of activity for each patient is calculated automatically through the app at the smartphone. The calculation is performed by using the data collect with the accelerometers embedded in the smartwatch. An algorithm installed in the phone, which analyze the data collected with the smartwatch, can calculate the level of activity for each patient throughout the day.
  
  
• Number of falls per patient registered by the falls detector. [ Time Frame: Patients will be automatically assessed during the follow-up time (up to 13 weeks after the enrollment date), 24 hours a day, 7 days a week. ]
  The fall event is recognized by the falls detector. Every time that the patient falls, the algorithm embedded at the falls detector recognize as a fall and record the fall event. At the end of the follow-up time, a sum of the falls event for each patient will be done.
  
  

Rationale: Today's management of patients with a chronic disorder like Parkinson's disease (PD) is imperfect. The understanding of clinical profiles is based on observations in small, selective populations with brief follow-up. Moreover, treatment decisions are based on averaged population results that may not apply to a specific individual context. These drawbacks will be addressed with a "big data" approach. Ambulatory sensors will be used as an objective measure of patients' performance under everyday circumstances, for longer periods of time. The researchers aim to explore the potential of using longitudinal ambulatory data to enrich a standardized clinical dataset, which reflects current clinical practice for the assessment of disease status.

Objective: The study will include a total of 250 physiotherapists and 1000 patients. The aims of this study are: (1) to perform "big data" analyses on the raw sensor data, in relation to concurrently acquired clinical data in these patients (limited version of the PPMI -Parkinson's Progression Markers Initiative protocol) to develop patient profiles; and (2) to correlate the ambulatory sensor data to simple self-assessments made during follow-up.

Study design: Observational descriptive study. Study population: Dutch Parkinson patients, male or female, age 30 years or older, with PD diagnosis given by a physician, and own a suitable smartphone.

lntervention: 250 ParkinsonNet physiotherapists and 1000 eligible patients will be included in this study. Patients and physiotherapists will be recruited in 5 consecutive cohorts based on geographic region. Patients will be asked to wear a smartwatch and a pendant movement sensor, both with triaxial accelerometers, during day and night, for a period of 13 weeks. Additionally, a self-monitoring App on a smartphone is used, where the patient reports when (s)he takes any PD medication. An additional, optional button allows the patient to report general feeling. During the 13 week follow-up, trained physiotherapists will perform a standardized clinical assessment, based on the PPMI protocol (www.ppmi-info.ors) for every included patient. This assessment will last for 60 minutes. The smartphone is used to transmit data from the watch to a cloud-based data platform. lntel developed this dedicated data analysis platform for ambulatory data. lntel will receive coded data only.

Main study parameters/endpoints: Study endpoints include parameters registered with the smartwatch, the pendant movement sensor, the self-monitoring app and collected with the PPMI assessment. The smartwatch data provides, after data processing, a measure for the level of physical activity during the day. Falls will be registered with the pendant movement sensor. Medication intake and mood are registered using the smartphone. Finally, PPMI assessment includes assessment of motor symptoms, cognition, depression, sleep and daily activity. Correlations will be determined between the above mentioned parameters.

Nature and extent of the burden and risks associated with participation, benefit and group relatedness: First, participants are asked to wear the devices 24/7 and data will be recorded continuously, for a total duration of 13 weeks. Second, data will be transmitted to a data platform developed and managed by lntel, on behalf of the Michael J. Fox Foundation for Parkinson's Research. To access these data, researchers can grant permission for research purposes, provided by Michael J. Fox Foundation. Patients will be asked for permission to share the raw coded data for dissemination to the research community, analysis and use in future publications. Participation in the study warrants that patients provide written permission for this.